METHOD FOR MANAGING HEALTH-RELATED DATA

专利摘要:
computer-deployed method for managing health-related data. a computer-deployed method for managing health related data captures, an image from an exercise machine display using a camera, has the images processed to extract text data from the captured images, and analyze the data to identify information related to extrinsic physical activity performed by a person on the exercise machine. the results are stored in memory and a person-specific profile is updated. the profile comprises a record of past exercise activity that allows the person to track their activity and progress and overall health. the profile can be accessed by the person through a portal, such as using a smartphone or a computer program or internet browser. the results can be combined with other data to arrive at a health score that can be published through this portal while the personal data remains masked from public inspection.
公开号:BR112013029467B1
申请号:R112013029467-1
申请日:2012-05-14
公开日:2021-06-15
发明作者:Peter Ohnemus；Andre Naef
申请人:Dacadoo Ag；
IPC主号:

专利说明:

Field of Invention
[0001] The present invention relates to a device to track a route and determine the energy expenditure of a person along such route. More specifically, the invention relates to a device that collects information about a person, exercise event details, and horizontal and vertical distance data from a route to calculate the energy expenditure of a person traveling along the route. Invention History
[0002] As a person engages in exercise activity, it is helpful for the person to be able to know how much energy the person exerted during that activity. Typically, exercise equipment in gyms, such as treadmills and stationary bikes, is able to calculate and display the energy expended by a person during exercise activity in the form of calorie burn data. The user only needs to enter their weight information into the machine in order to estimate the calories burned. However, these machines operate in a controlled environment. The type of activity that can be performed on the machine (cycling or running) is dictated by the type of machine (bike or treadmill). The speed and elevation (treadmill angle or resistance on the bicycle) are controlled by the machine and thus known by the machine. Furthermore, the “terrain” is uniform on these machines because the exercise surfaces do not change (the treadmill walking surface does not change to sand). As such, it is relatively easy for these machines to calculate the energy expended in these controlled environments. These machines are well suited to reporting exercise information pertaining to the user, however, a disadvantage is that the information is temporary. The information is displayed by the machine for a short time, and, in most instances, personal recording of information is only possible on a single machine. In more advanced systems in gyms, there may be a registration system that can capture training information across multiple machines in the gym, however, this system is limited to that specific gym location or gym chain. It is desirable to be able to record workouts across a variety of machines and in a multitude of locations, such as at home or at the gym or while on vacation.
[0003] Many people desire to exercise outdoors and don't want to be confined to a machine in a gym. Outdoor exercise is fraught with many variables such as changes in terrain. Furthermore, distance travelled, speed and elevation changes are not controlled by a machine. This presents a layer of difficulty when determining the energy expended on an outdoor exercise route, as these parameters must be measured. With the proliferation of mobile and smart electronic devices such as smartphones, measuring these parameters has become easier. Smartphones typically have the ability to determine position using GPS modules. Software applications that use the phones' GPS feature can calculate distance traveled and speed during an exercise route. Software applications can also use the GPS feature to calculate changes in elevation during the exercise route. If the user enters their weight, software applications can calculate calories burned during the exercise route.
[0004] These existing applications have several disadvantages, however. For example, these systems rely on GPS to determine changes in elevation. While GPS can determine latitude and longitude relatively accurately, GPS systems are less accurate when calculating elevation. Correspondingly, systems that rely on GPS to determine elevation changes during the exercise route, which in turn are used to calculate calories burned, suffer from accuracy issues. Furthermore, these systems are limited to a specific activity, such as running. These systems cannot be used for different activities such as running, cycling, skiing, etc. These systems are also limited in information about the activity that is considered to have a major impact on energy expenditure calculations.
[0005] The present invention addresses these and other problems. Description of Drawing Figures
[0006] Figure 1A illustrates an exemplary diagram of a mobile electronic device in wireless communication;
[0007] Figure 1B is a block diagram illustrating certain components of the mobile electronic device and a remote server;
[0008] Figure 1C is an exemplary diagram of a system for managing health related data;
[0009] Figure 2 is a flowchart illustrating a process of calculating energy expenditure;
[0010] Figure 3 is a flowchart illustrating a computer-deployed method for managing health related data;
[0011] Figure 4 is a schematic block diagram of a local health information collection and communication system according to a first implementation of the invention;
[0012] Figure 4A is a network diagram according to another implementation of the invention;
[0013] Figure 5 is a schematic flowchart according to an embodiment of the invention;
[0014] Figures 6a-6e are screen shots of a user interface according to an embodiment of the invention;
[0015] Figure 6f is an illustration of time progressions of the parameters used to determine the health score in an embodiment of the invention;
[0016] Figure 7a is an illustration of a data presentation format according to an embodiment of the invention;
[0017] Figure 7b is an illustration of a data presentation format according to an embodiment of the invention;
[0018] Figure 7c is an illustration of a data presentation format according to an embodiment of the invention; and
[0019] Figure 7d is an illustration of a data presentation format according to an embodiment of the invention. Invention Summary
[0020] According to one aspect of the present invention, a method deployed by health related data as claimed in claim 1 is provided.
[0021] In one embodiment, the method includes receiving data into a memory related to a plurality of extrinsic physical activity parameters and capturing an image from a display that is coupled to an exercise machine. The image can be captured, for example, with a smartphone camera. According to this method, the images are then processed to extract text data from the captured images, and the extracted text is analyzed to identify information related to the extrinsic physical activity performed by the person using the system on the exercise machine. Results are stored in memory and a person-specific profile is updated. The profile comprises a record of past exercise activity that allows the person to track their activity, progress and overall health. The profile can be accessed by the person through a portal, such as using a smartphone, a computer program or a web browser.
[0022] In another arrangement, text can be extracted using an optical character recognition algorithm that distinguishes text characters from other image data and records the text character, its location and other spatial properties.
[0023] In another arrangement, the text extracted from the captured image can be analyzed to identify the sequences in the text. This can be accomplished by identifying characters that are in close proximity and of the same type (i.e., letter or number) and grouping those characters together. Sequenced data can be further manipulated and analyzed by processors and classified into categories such as numbers, duration and units. Furthermore, the spatial relationships between various sequences can be determined by the system.
[0024] According to an additional aspect of such a method, the image can be analyzed to identify the brand of the exercise machine manufacturer. In addition, the method may further comprise the step of extracting text from a limited image area as dictated by the brand of exercise machine.
[0025] In one embodiment, a computer-implemented method for processing private health-related data into a masked numeric score suitable for publication is provided. The method comprises receiving data into a memory on a plurality of intrinsic medical parameters and extrinsic physical activity parameters of a user. Received data and weighing factors are stored in memory. Received data is processed by running code in a processor that configures the processors to apply weighing factors to intrinsic medical parameters and extrinsic physical activity parameters. The weighing factors for at least the extrinsic physical activity parameters include a decay component arranged to reduce the relative weight of the extrinsic physical activity parameters to a physical activity in dependence on at least one factor associated with the user. The processed data referring to intrinsic medical parameters and extrinsic physical activity parameters are transformed by an additional code running on the processors into a masked composite numeric value in which the code is operative to combine the weighted parameters in accordance with an algorithm. The masked composite numeric value is automatically published to a designated group via a portal (such as a social website) using code running on the processors and free of any human intervention. Meanwhile, the information collected regarding intrinsic medical parameters and extrinsic physical activity parameters is kept private.
[0026] According to an additional aspect of such a method, as may be implemented in a specific realization thereof, the factor associated with the user may be an age or a range of age of the user so that the decay component reduces the weight relative of the extrinsic parameters of physical activity is a first user of a first age or age range differently than a second user of a second age or age range.
[0027] According to yet another aspect of such a method as may be deployed in a specific embodiment thereof, the published masked composite numeric value may comprise an average of a group of users to arrive at a group composite numeric value determination using code additional running on the processors.
[0028] In one embodiment, a computer-implemented health monitoring system is provided comprising a communication unit operable to receive data on a plurality of intrinsic medical parameters and extrinsic parameters of a user's physical activity. A memory is arranged to store received data and store weighing factors. Likewise, a processor is arranged to process the received data by executing code that configures the processor to apply weighing factors to intrinsic medical parameters and extrinsic physical activity parameters. The weighing factors for at least the extrinsic physical activity parameters include a decay component arranged to reduce the relative weight of the physical activity parameters for a physical activity in dependence on at least one factor associated with the user. The processor is further arranged to execute code so as to transform the processed data relating to intrinsic medical parameters and extrinsic physical activity parameters into a masked composite numeric value using the processor combining the weighted parameters in accordance with an algorithm. A portal is willing to publish the composite numeric value masked to a designated group while maintaining the collected information regarding the intrinsic medical parameters and extrinsic parameters of private physical activity.
[0029] Such a system can preferably be configured so that the factor associated with the user can be an age or an age range of the user so that the decay component reduces the relative weight of the extrinsic physical activity parameters for a first user of a first age or age range differently than a second user of a second age or age range. Detailed Description of Certain Achievements of the Invention
[0030] The following detailed description, which references and incorporates the drawings, describes and illustrates one or more specific embodiments of the invention. These embodiments, offered not to limit but only to exemplify and teach the invention, are shown and described in sufficient detail to enable those skilled in the art to practice the invention. Thus, where appropriate, to avoid obscuring the invention, the description may omit certain information known to those skilled in the art.
[0031] In an implementation, with reference to the Figures. 1A and 1B and 1C, a system 100 for determining a person's energy expenditure includes a mobile electronic device 102 and a remote server 104.
[0032] The mobile electronic device 102 may be a cell phone, personal digital assistant, smartphone, tablet computing device, or other portable electronic device. Mobile electronic device 102 includes a control circuit 103 that is operatively connected to various hardware and software components that serve to allow determination of the energy expenditure of a person traveling along a route and/or to determine extrinsic physical activity parameters of a person exercising on an exercise machine 107, as discussed in more detail below. Control circuit 103 is operatively connected to a processor 106 and a memory 108. Preferably, the memory 108 is accessible by the processor 106, thus allowing the processor 106 to receive and execute instructions stored in the memory 108.
[0033] One or more software modules 109 are encoded in memory 108. Software modules 109 may comprise a software program or set of instructions executed on processor 106. Preferably, software modules 109 build an exercise monitoring application which collects data, ie, extrinsic physical activity information, which can be used to calculate energy expenditure, and perform other functions, which is performed by the processor 106. During the execution of the software modules 109, the processor 106 configures the circuit control panel 103 to collect information about the person and the person's exercise route, communicate position details about the exercise route for the purpose of receiving elevation details and other functions, as discussed in more detail below. During execution of the modules, processor 106 can also configure the control circuit to collect image data from a camera. It should be noted that while FIGURE 1B illustrates memory 108 in control circuit 103, in an alternative arrangement, memory 108 can be virtually any storage media (such as a hard disk drive, flash memory, etc.) that it is operatively connected to the control circuit 103, even if not oriented in the control circuit as illustrated in FIGURE 1B.
[0034] An interface 115 is also operatively connected to the control circuit 103. Interface 115 preferably includes one or more input devices such as a switch, knob, button(s), switch(es), touch screen, etc. Interface 115 is operatively connected to control circuit 103 and serves to facilitate the capture of certain profile information and details about the user's exercise event, as discussed in greater detail below. By way of example, the input device of interface 115 may be a touch screen display. Correspondingly, display 114 is used to display a graphical user interface, which displays various data and provides “shapes” that include fields that allow the user to enter any additional information. Touching the touchscreen interface 115 at locations corresponding to the graphical user interface display allows the person to interact with the device to enter data, change settings, control functions, etc.
[0035] Then, when the touch screen is touched, the interface 115 communicates this change to the control circuit 103, and settings can be changed or user input information can be captured and stored in memory 108.
[0036] The display 114 includes a screen or any other such presentation device that allows the user to view various options and parameters, and select among them using the aforementioned interface 115. In yet another arrangement, one or both of interface 115 and display 114 may be deployed in a non-visual and/or non-tactile manner, such as, by using a series of audio menus and/or voice commands/commands to select and/or configure settings, provide user and exercise event information, and/or control system functions.
[0037] In one arrangement, the interface 115 still allows for setting the settings and entering information when initiating and/or maintaining one or more communication sessions with an external device that is communicatively connected to the mobile device 102. In one arrangement, interface 115 can connect with an external personal computer (PC) via a USB connection, Bluetooth connection or any other means of connection/communication. The user can then use the connected PC to configure user settings, profile data, etc., and/or upload or otherwise communicate new settings, profile data, etc. that the user previously defined and/ or obtained from an external source (such as the Internet). In another arrangement, interface 115 can connect to an external storage device, such as a USB flash drive, and receive one or more settings that are stored there. In yet another arrangement, interface 115 via communication interface 110 can connect to one or more external servers via a network connection. For example, interface 115 may utilize a pre-existing network connection, such as an Internet connection, via communication interface 110 using a wireless connection. By doing so, the interface can connect to several remote servers that contain settings that are available for users to download. The user can download one or more desired configurations and store them in memory 108. This functionality of the interface 115, which allows the user to obtain and/or update the set of user configurations, profile data, event data, etc., stored in memory 108, is of particular use when used to obtain and/or update settings pertaining to specific exercise equipment (eg bike weight is pre-stored) or exercise route information (p .eg, details about a person's favorite exercise routine, such as terrain information, etc., can be pre-stored so that they do not have to be re-entered each time the person follows the same routine) .
[0038] A positioning device 112 is operatively connected to the control circuit 103. The positioning device 112 may be a global positioning system (GPS) circuit or a positioning system that relies on triangulation between cell phone towers for the purpose of determining the position. The positioning device 112 allows the determination of the location of the mobile device 102 and, consequently, the position of the person. Using the positioning device 112, the position coordinates (eg latitude and longitude) of the person can be determined.
[0039] A communication interface 110 is operatively connected to the control circuit 103. The communication interface 110 may be a cellular communication circuit allowing communication with a cellular network 116, a Wi-Fi communication circuit allowing communication directly to the internet 118 via a Wi-Fi connection, and/or a circuit enabling communication with a computer terminal 120, such as a Bluetooth® circuit and/or circuit enabling wired communication.
[0040] The control circuit 103 can also be operatively connected to a camera 117. The camera 117 can be any type of digital camera, including, but not limited to, a camera found on a smartphone or cell phone. The camera can be used to capture the digital images of the user display 111 of an exercise machine 107 which displays exercise activity related information. By way of example, the information can be related to the amount of energy that was expended by the person, in the form of calorie burn data or another unit of energy, the information can also relate to the distance traveled while on the machine. The control circuit is configured to store the digital images that are captured by the camera in memory 108. The processor 106 is configured to analyze the images and extract the extrinsic physical activity parameters related to the user's exercise activity.
[0041] Referring to Figure 1A, an exemplary diagram illustrates mobile electronic device 102 preferably in wireless communication with communication network 116, such as a cellular communication network. The communication of the mobile device 102 with the communication network 116 facilitates the connection to the internet 118. The remote server 104 is also connected to the internet 118. Correspondingly, the mobile electronic device 102 can communicate and transmit data and receive data to from remote server 104 via communication network 116 and internet 118.
[0042] The mobile electronic device 102 can also communicate with a computer terminal 120. The computer terminal 120 can be a personal computer, for example. Mobile electronic device 102 can communicate with computer terminal 120 via a Wi-Fi or Bluetooth connection, for example. Mobile electronic device 102 can also communicate with computer terminal 120 via a wired connection, using a USB cable, for example. The computer terminal 120 is connected to the internet 118. In this way, the mobile electronic device 102 can communicate with the remote server 104 via a computer terminal 120. The mobile electronic device 102 can also communicate with the internet 118 via its communication interface 110 (eg Wi-Fi) and thus connect to the remote server 104.
[0043] The server 104 includes a processor 122, a database 124 and a communication interface 126. The database 124 includes the topographical data. Topographic data can be in the form of topographic maps that include contour lines and elevation data. Each contour line represents an elevation range. For example, if the contour lines represent a ten-foot range, crossing ten contour lines between two points on a topographic map represents a 100-foot elevation change. The distance between the contour lines on the map represents the slope of the terrain. The closer the contour lines together, the greater the slope of the terrain. Topographic maps can be digital data that include elevations at known coordinate points on the map. Topographic maps and data can be used to determine elevation at a given position. As discussed in more detail below, remote server 104 can receive position coordinate data from mobile electronic device 102, correlate the position coordinates with the topographic map data, and determine elevation at such position. The remote server 104 can transmit the elevation value corresponding to the position coordinate back to the mobile electronic device 102.
[0044] The operation of the mobile device 102 and various elements described above will be appreciated with reference to the method for calculating the energy exerted by a person throughout an exercise routine, as described below, in conjunction with Figure 2.
[0045] Referring now to Figure 2, a flow diagram illustrates the proper functionality to capture information about a person, the person's routine, and other information, in order to determine the amount of energy the person spends traveling along such a routine. System 100 can be used by the person to calculate the energy expended by the person as they move through a routine, such as an exercise routine. It should be appreciated that the various logic operations described herein are implemented (1) as a sequence of the acts implemented by computer or program modules executing in mobile device 102 and/or (2) as interconnected machine logic circuits or circuit modules within the device. mobile 102. Deployment is a matter of choice depending on device requirements (eg, size, power, consumption, performance, etc.). Correspondingly, the logical operations described here are referred to interchangeably as operations, structural devices, acts, or modules. Several of these operations, structural devices, acts, and modules can be deployed in software, firmware, special-purpose digital logic, and any combination thereof. It should also be appreciated that more or less operations can be performed than those shown in the figures and described herein. These operations can also be performed in a different order than that described here.
[0046] In step 200, a person starts the device to calculate energy expenditure. Preferably, a person starts the system before beginning exercise activity that involves walking along a route (eg, walking, running, walking, cycling, snow skiing, cross-country skiing, etc.).
[0047] In step 202, the person's profile is displayed. A profile contains various physiological and other health-related information about the person. Profile information can include the person's age, weight, height, body mass index, physical ability information (eg, information about a person's ability to complete physical tasks (running speed, endurance, running ability). weight lifting, etc.)), medical history (eg medical conditions such as diabetes, heart disease, high blood pressure, cholesterol levels, lipid levels, etc.). At least some or all of this information can be used in the calculation to determine the energy the person exerted as a result of traveling the route, as will be discussed in more detail below.
[0048] The person is asked to update their profile in step 204. For example, a person may have lost or gained weight since the last time their profile was updated. Correspondingly, the person is presented with an opportunity to correct such information and profile changes are captured in step 208. If the profile is missing data or is completely empty (eg, the first use of the system by this person), the person can provide the information required to complete the profile.
[0049] In step 210, the details about the exercise event are captured. For example, environmental data such as temperature data, wind speed and wind direction data, humidity data, etc. can be captured. The system can request the mobile electronic device 102 to determine the position of the mobile electronic device using the positioning device 112. The system can then connect to the internet using its communication interface 110 and transmit the position data to a database weather service or weather service website available on the internet. The mobile electronic device 102 can then receive environmental data from the weather service that correlates to that position. The person can also manually enter the environmental condition information. Environmental conditions can affect the energy exerted during the exercise event. For example, high temperatures can cause a person to exert more energy during exercise, or wind direction and speed (ie, tail wind or headwind) can affect energy effort, especially during activities such as cycling. . Environmental data can be used to generate a weighting factor that will increase or decrease certain values in the energy usage calculation. Environmental data can also be used as a variable in the energy expenditure calculation (eg wind speed in the cycling calculation, as discussed in more detail below).
[0050] In addition, the person may be asked to enter additional information about the exercise activity they are performing. For example, they can indicate whether they are hiking, running, biking, walking, cross-country skiing, snow skiing, skating, skateboarding, etc. More energy will be exerted by walking a certain distance than cycling a certain distance, and the algorithms used to calculate such energy expenditure may be different. Correspondingly, selecting an activity type assigns a parameter value and the system checks the parameter value to select the proper algorithm to determine energy expenditure. Also, selecting the activity type can control the system's functioning. For example, sports such as tennis, squash, baseball, football, football, etc., are typically played on flat fields of limited dimensions. These types of activities typically do not involve changing elevation as the person moves across the field. In this way, the elevation measurement becomes less important when determining the energy expended. Correspondingly, a parameter can be set to a value of zero for activities that are defined as not involving elevation change (eg, football) and a non-zero value for activities that are defined as potentially involving elevation changes. lifting (eg, running). The system can then check the parameter, and if it is zero, the system skips the procedure of transmitting the location information to a remote server and receiving the elevation information (steps 232 and 234, discussed in more detail below), and assumes that the elevation change is zero. If the parameter is non-zero, the system performs the elevation determination steps. In addition, the type activity can also control how the system determines the distance traveled. For example, tennis is performed on a relatively confined court and using GPS alone to determine the distance covered associated with movement back and forth across the court may not produce the most accurate results. In this way, the system can also use an accelerometer to determine the person's amount of movement. The selected activity type has a predetermined value associated with it, and depending on the value, the system can use different algorithms and methods to determine the distance covered using accelerometer data.
[0051] The person can also enter the route terrain information, such as, if the land surface is paved road, gravel road, dirt road, woodland path with forest fragments, loose gravel, loose sand, etc. The type of terrain can affect the energy exerted by a person along a route. For example, more energy is exerted traveling through the area than running on a paved road. The system can have stored values associated with the type of terrain that can be used in calculating energy expenditure. The person can also enter information about the type of equipment they are using, such as whether they are on a mountain bike, which has thick, rough tires with less pressure and therefore greater resistance, or a road bike, which it has thin, soft tires at high pressure and, consequently, less rolling resistance. For example, the system may have stored friction values for different tires or tire widths, diameters, tread types and inflation pressures. The person can also indicate if they are carrying any equipment, such as a backpack with supplies, and the weight of the package. In this way, terrain, environmental, and equipment information can be included in the calculation (as a weighting factor or a variable) to determine energy use for the purpose of increasing the accuracy of the cost calculation.
[0052] In step 212, the person, through the mobile electronic device interface 102, indicates the start of the event. This action indicates that the person has started their exercise and will progress along an exercise route. Correspondingly, in step 214, the position of the person is captured using the positioning device 112 of the mobile electronic device 102. As discussed above, the mobile electronic device 102 includes a positioning device 112 that determines the position of the person. Positioning device 112 may be a global positioning system (GPS) module or a positioning system that relies on triangulation between cell phone towers for the purpose of determining position. Positioning device 112 allows mobile electronic device 102 to determine the position coordinates (e.g., latitude and longitude) of the mobile electronic device, and hence the person carrying the mobile electronic device.
[0053] In step 216, the time is captured that corresponds to the time the position was captured in step 214. The corresponding position and time data are then stored in the memory 108 of the mobile electronic device 102. This creates a tracking point , which is a record of the person's position and the time the person was in that position. These tracking points can be used to determine the person's distance traveled and speed during the exercise event, as discussed further here.
[0054] In step 218, the person is presented with an option to view their current progress during the exercise event. If the person does not request an intermediate display of progress, the person can continue with the exercise.
[0055] The person can indicate whether the exercise event is complete, at step 220. If the event is not complete, the system optionally waits for an interval to elapse at step 222, before returning to step 214 of capture position 214. The interval can be a distance interval and/or a time interval. The GPS module can create a notification that the device has moved a certain distance, which satisfies the “interval” in step 222 and caused the position to be captured in step 214. Additionally, or alternatively, the interval can be a time interval and, after the predetermined interval has elapsed, the system proceeds to step 214 and the position is captured. The time interval between successive position captures can be set to a longer time interval in order to reduce the number of processing cycles and therefore preserve power and battery life. The time interval can also be set for a short time if more data points are desired, which will result in more accurate information about the person's exercise route. The system follows this loop and collects successive position and time data (tracking points) of the person during the person's exercise event.
[0056] If the person requests an internal display of progress in step 218 or indicates that the exercise event is complete in step 220, the system then proceeds to energy expenditure analysis in step 224. Alternatively, energy expenditure analysis energy can be held after each location capture. Correspondingly, the system proceeds to steps 224 after each location capture, even if the user does not request an interim display in step 218 or indicate an end condition in step 220.
[0057] In step 226, the system determines the change in horizontal distance between successive tracking points along the route. This is done by calculating the difference in position coordinates (latitude and longitude) between a first position and a previous second position. This difference represents the horizontal distance traveled by the user between successive tracking points. This process is repeated for all successive tracking points and all positioning deltas can be added up to arrive at the total horizontal distance traveled by the person during the exercise event. This represents the horizontal distance traveled by the person along the route. For example, the horizontal distance between two tracking points (a current tracking point and a previous tracking point) can be calculated using the spherical law of cosines using the formula:

[0058] The spherical cosine law is accurate to about 0.3% - which is likely to be sufficient considering the general inaccuracy of GPS. However, there are also more accurate numerically stable calculations based on a suitable WGS84 ellipsoid, which can be used in other arrays.
[0059] In step 228, delta time is calculated by considering the difference in times between two tracking points. The total exercise event time can be calculated by aggregating all delta times between all successive tracking points. In step 230, the person's average speed between two tracking points can be calculated by dividing the delta distance by the delta time between the tracking points.
[0060] In step 232, the position information for one or all of the tracking points is transmitted to a remote server 104 using the communication interface 110 of the mobile electronic device 102. The remote server 104 includes a database that includes the topographical data (ie, data that includes elevation for specific geographic points). Remote server 104 receives position data from mobile electronic device 102 and then correlates such position data to topographical data for the purpose of determining the elevation corresponding to that position. As such, the system relies on topographic map data to determine elevation at a tracking point as opposed to using GPS to determine elevation. The present system offers significant advantages over systems that rely on GPS calculation to determine elevation because such GPS systems have higher levels of inaccuracies.
[0061] The present system uses position coordinates and then uses topographic map data (eg United States Geological Survey data), which typically has a high degree of inaccuracy, for the purpose of determining elevation of the person in a specific position. The elevation in the position coordinate of a tracking point can be calculated in a number of different ways using a number of different methods. One method of calculating elevation corresponding to a tracking point relies on digital elevation model data where elevation information is provided only at specific grid positions. Correspondingly, if the position of a tracking point (latitude and longitude) does not match an existing grid position, the elevation must be calculated using the adjacent grid and interpolation positions to determine the elevation at the tracking point. The elevation (height) of a specific tracking point can be calculated using variables and equations, as follows:

[0062] The weight of the four grid lines adjacent to the position of the tracking point can be calculated as follows:

[0063] The weight of the four grid corners adjacent to the position of the tracking point can be calculated as follows:

[0064] The interpolated height of the tracking point can be calculated as follows:

[0065] Correspondingly, the height (elevation) of each tracking point can be calculated using the digital terrain elevation models corresponding to the tracking points along the person's exercise route.
[0066] The mobile electronic device 102 then receives the elevation values from the remote server 204 via its communication interface 110 at step 234. This process is repeated for each track point captured along the route.
[0067] As shown in Figure 2, the steps of transmitting position(s) 232 and receiving elevation values corresponding to position(s) 234 are performed during the energy expenditure analysis process 224, which is triggered when the user requests an internal progress display at step 218 or indicates that the exercise event is finished at step 220. However, transmitting (232) to the server and receiving the elevation values (234) after the server calculates the values for all stored tracking points can be a time-consuming process. Correspondingly, as shown in dotted lines, steps 232 and 234 may alternatively be performed after each position capture step 214. Correspondingly, the system transmits positions and receives elevations as position information is captured. This way, when the user requests a progress display or indicates that the exercise event is complete, the system relies on the already stored elevation data, which can reduce the time to calculate energy expenditure.
[0068] In step 236, the delta elevation or change in vertical distance between successive tracking points is calculated.
[0069] In step 238, the actual distance traveled by the person is calculated using the delta distance (horizontal) data and delta (vertical) elevation data calculated in steps 226 and 236, respectively. The effective distance can be calculated using the formula:

[0070] The person's energy expenditure between the tracking points is calculated in step 240 using an algorithm, the profile data (202/208), the event detail data (210) and the calculated effective distance (238) of the exercise route. The algorithm that is used to calculate energy expenditure can be depending on the type of activity performed by the person. For example, cycling characteristics require different factors to be considered to determine energy expended. Cycling usually involves higher speeds that make wind speed and air resistance relevant (eg, riding into a strong wind requires more work). Cycling also requires consideration of tire rolling friction, which is dependent on tire type and tire pressure. In cycling, a cyclist can descend, which requires less work from the cyclist, whereas coasting is not possible in the race. An exemplary algorithm and variables for determining energy expenditure during cycling are provided as follows:

[0071] The force (F) can be calculated as follows:

[0072] Using the force (F) calculated above, the energy expended (E) by the cyclist can be calculated as follows:

[0073] The cyclist's spent power (P[exp]) can be calculated as follows:

[0074] The effective power (P[eff]) of the cyclist can be calculated as follows:

[0075] An exemplary calculation of a cyclist weighing 90 kg, climbing an incline of 5% for 1 kilometer at a speed of 12 km/h, using a hybrid bicycle with a well-inflated tire on a clean, paved road, is provided as follows:

[0076] Calculations are as follows:

[0077] As discussed above, due to the different characteristics of certain exercise activities, different algorithms can be used to accurately calculate the energy expended by a person during an activity. Also, different algorithms can be used depending on whether the person is moving up or down along the route. For example, a different algorithm can be used if the person is pedaling downhill, as cost becomes a factor in the determination. A person who is coasting exerts less energy than a person who is pedaling uphill over a certain distance. Accordingly, the result of energy expenditure for downward sections can be reduced by a weighting factor (eg, reduced by (50%) for downward sections. Alternatively, the terminal velocity for a given slope, considering friction , wind resistance, etc., can be calculated assuming no cyclist pedaling (ie coasting at 100%) Then the actual speed at the end of the incline can be measured If the actual measured speed is greater than the calculated terminal speed (meaning the cyclist had to contribute energy to account for the increased speed), the amount of energy required to reach the excess speed can be calculated.
[0078] As an example of another algorithm that can be used for a different activity, one method of determining energy expenditure during running or walking is based on the oxygen consumption of the runner/pedestrian. An exemplary algorithm and variables for determining energy expenditure during the run/walk are provided as follows:

[0079] Oxygen consumption (VO2) can be calculated as follows:

[0080] Using the oxygen consumption (VO2) calculated above, the energy expended (E) by the runner/pedestrian can be calculated as follows:

[0081] The spent power (P[exp]) of the runner/pedestrian can be calculated as follows:

[0082] An exemplary calculation of a runner weighing 82 kg, climbing an incline of 5% for 1 kilometer at a speed of 8 km/h, on a clean and paved road, is provided as follows:

[0083] The calculations are as follows: VO2 = 0.2 + 0.9 * 0.045 = 0.2405 [mL / (kg * m)] E = 0.2405 * 82 * 1000 * 21.1383 = 416868 [J ] = 99.6 [kcal] P[exp] = 917 [Watt]
[0084] These are just two examples of algorithms and factors that can be used to determine energy expenditure during a given exercise. Other variables such as ambient temperature can also be included in calculations. They can be included as separate variables in the calculation and/or they can be included as weighing factors. For example, exercising in hot weather results in increased energy expenditure. In this way, for each degree above an ideal temperature, the calculated energy expended can be increased by multiplying it by a weighting factor. For example, for every degree Fahrenheit above 60 degrees, the amount of energy expended can be increased by 1% by multiplying it by a weighting factor.
[0085] The energy expended during this exercise event is then added to the person's profile in step 242. This energy expenditure information can also be used to calculate a person's overall health score, as described more fully in the patent application. provisional co-pending serial number 61/387,906, filed on September 29, 2010, and named HEALTH DATA ACQUISITION, PROCESSING AND COMMUNICATION SYSTEM. The person's updated health score can also be posted on a social networking site as described in application 61/387,906 so that others can see the person's health score and/or exercise activity.
[0086] In step 244, exercise event statistics can be sent to the graphical user interface and displayed on display 114 of mobile electronic device 102. Statistics can include total distance traveled, total elevation change, total time, speed average and energy spent.
[0087] The system ends at step 246 if the user indicated that the exercise event had ended at step 220. If the user requested an internal progress display in step 218, and the exercise event is not yet complete, the system returns to step 214 to begin collecting additional tracking points as the person continues the exercise event.
[0088] There are many possible variations to the steps described above with respect to Figure 2. For example, as described above, the position data is transmitted to the remote server and the elevation corresponding to such position is received by the mobile device. Alternatively, the position data can be transmitted to the remote server and the mobile electronic device can receive the digital elevation model data with grid points from the server. In this arrangement, calculations to interpolate height in positions can be performed by the mobile device rather than the remote server. In another arrangement, profile data, event detail data, position data and time data can be transmitted to the remote server. The remote server can then perform all the calculations needed to determine the energy expended by the person. Then, the mobile device can receive the energy spent values from the server and display them without having to perform the calculations.
[0089] System 100 can also be used to predict the energy a person would expend if the person exercised along a specific route. The predictive function can be used by the user to determine which exercise route is best suited to the user's exercise goals. For example, the user can use the mobile device 102 to retrieve the map data from the internet 118. Users can then define a starting point and an ending point on the map and the device can present several different possible exercise routes between the points. starting and ending. Different routes have different distances, different elevation changes, and different terrain conditions. This information may be contained in map data retrieved from the internet. The user can then indicate the type of exercise (eg cycling, running, etc.) that the person intends to engage in and the intended speed of the exercise. The system then divides the proposed routes into a number of position tracking points. Position tracking points are then transmitted to the remote server. The remote server calculates the corresponding elevation information, as discussed above, and the device receives the elevation information from the server. The device then calculates, as discussed above, the predicted energy that would be expended for the selected activity along each proposed route. That way, if a person has a goal of expending a certain amount of energy (eg, burning 1000 calories), the person can select a route that is predicted to reach that goal.
[0090] Referring now to Figure 3, a flowchart illustrates proper functionality for managing health related data captured from an exercise machine with a camera. System 100 can be used by the person for the purpose of capturing extrinsic parameters of physical activity from a display 111 of an exercise machine 107 and recording such information in a personal record containing historical exercise activity information.
[0091] In step 300, a person captures an image that includes the display 111 of an exercise machine 107 using a mobile device 102 with a camera 117 or a different type of camera. Exercise machine 107 is not limited to a specific type of machine, such as a treadmill, exercise bike, elliptical trainer, rowing machines, and exercise machine 107 is not limited to a specific manufacturer or model.
[0092] In step 310, processor 106, running one or more software modules 109, is configured to store the digital image captured by camera 117 in memory 108. The image can be stored locally on mobile device 102 or remotely on another device , such as a server 104. Similarly, further processing by processor 106 on the image may be performed locally or remotely, or a combination of remote and local processing.
[0093] In step 320, the processor 106, executing one or more software modules, 109, is configured to determine, from the image, the extrinsic parameters of physical activity that were shown on the display 111 of the exercise machine 107.
[0094] In an array, the extrinsic physical parameters can be determined by implementing an optical character recognition (OCR) algorithm. OCR algorithms, which are known in the art, are used to distinguish text characters from other image data (e.g., non-text trees, etc.) and record such information including the character and associated character location in the Image.
[0095] Processor 106 can be configured to implement an OCR algorithm in order to identify the characters in the image data and associated character location and a confidence score that corresponds to the probability of the algorithm being correct in identifying a specific character.
[0096] Processor 106 can also analyze OCR data, including characters, locations and confidence, using a sequencing algorithm. The sequencing algorithm can analyze the OCR data to find characters that are in close proximity and of the same type (i.e., letter or number) that must be grouped together. For example, the two numbers 1 and 0 next to the two letters k and m can be grouped together as independently as “10” and “km”. By way of additional example and not limitation, the grouping algorithm can also configure the processor to group numbers separated by “:” together, for example 1 0 : 5 5 is grouped together as “10:55”, instead of “10 ” “:” and “55”. The sequencing algorithm sorts raw OCR data into more meaningful sequenced data that can be further manipulated and analyzed by processors 106.
[0097] Processor 106 can be configured to further categorize sequenced data using a classification algorithm. The classification algorithm can compare the sequenced data with known types of information and categorize them accordingly. Some categories include numbers such as "112" or "198.8", durations such as numbers grouped together separated by a ":" or units such as letter groups and symbols such as "kcal" or “km/h”. Similarly, processor 106 can further categorize units into their specific types, including as "power" or "time" or "distance" by comparing the unit to a list of known units and types that are stored in memory 108.
[0098] Processor 106, executing one or more software modules 109, can determine whether character sequences that are categorized as a number or duration are associated with a specific unit by comparing the location of the number or duration with the location of the unit . For example, if the location of a unit is crossed in whole or in part by the location of a number or duration, vertically or horizontally, the unit and the number or duration can be associated. The amount by which a unit and a number or duration need to intersect to be associated with each other can be a predetermined percentage, for example 80%.
[0099] If there are multiple numbers and/or durations that intersect with a unit, the processor can be configured to associate the unit with the number or duration that is closest to the unit. If one or more durations are identified, however, there is no unit that is categorized as "time" associated with one or more durations, the processor 106, running one or more software modules 109, is configured to scan the area of one or more longer durations and determine the longest duration in terms of area. The processor can be configured to associate the greatest duration, in terms of area, with the elapsed training time.
[0100] In another arrangement, processor 106, running one or more software modules 109, is configured to extract extrinsic physical activity parameters from an image captured by camera 102 can also incorporate the step of first identifying the brand ( manufacturer's brand) and model of exercise machine 107 that was being used by the person. Processor 106 can be configured to implement a photo comparison algorithm to compare the image to a database of photos of known exercise machines that is stored in memory 108. Photo comparison algorithms are known in the art and work by compare the features of one image to another to determine the degree of similarity. If the image matches a photo of a known exercise machine, within a predetermined degree of confidence, the processor can analyze the image for extrinsic physical activity parameters according to a specific algorithm adapted to such exercise machine.
[0101] If the image does not match a photo of a known exercise machine within the predetermined degree of confidence, the processor 106 can be configured to prompt the user to confirm the make and model of the exercise machine or manually enter the manufacturer and model using the 115 interface, or ask the user to recapture the image with the camera and try again.
[0102] Determining the make and model of the exercise machine prior to analyzing the text information in the image can help reduce the amount of image data that processor 106 needs to analyze. For example, if a Precor™ treadmill is known to display the distance traveled during training in the upper right corner of the screen, and the amount of calories burned in the lower right corner, the system only needs to analyze the upper right portion of the image to locate and extract the information corresponding to the distance traveled and the lower right corner for the calories burned.
[0103] In step 330, the extrinsic physical activity parameters are stored in memory 108.
[0104] In step 340, the extrinsic physical activity parameters can then be added to the person's profile. The profile, as discussed above, contains the physiological and other health and exercise-related information about the person, and is a continuous record of the extrinsic physical activity parameters corresponding to previous training sessions that allow the user to track their activity, progress, and general health.
[0105] In step 360, the profile can be provided to the user through a portal. The portal may include, but is not limited to, an application on the person's mobile device or a web-based portal accessible via the internet.
[0106] Extrinsic physical activity parameters can also be used to calculate a person's overall health score, as described more fully below and in: Provisional Patent Application Serial Number 61/387,906, filed September 29, 2010, and WO Patent Application No. PCT/US11/53971 filed on September 29, 2011 called HEALTH DATA ACQUISITION, PROCESSING AND COMMUNICATION SYSTEM, all of which are incorporated herein by reference. The person's updated health score can also be posted on a social networking site as described in application 61/387,906 so that others can see the person's health score and/or exercise activity.
[0107] In another implementation, with reference to the Figures. 47, a system 1100 includes a computer-based application for collecting parameters related to a user's health and a user interface 1110 for displaying the data. The computer-based application is deployed via a microcontroller 1120 which includes a processor 1124, a memory 1122 and code is executed in order to configure the processor to perform the functionality here would descend. Memory is for storing data and instructions suitable for controlling processor operation. A memory implementation can include, by way of example and not limitation, random access memory (RAM), a hard disk, or read-only memory (ROM). One of the components stored in memory is a program. The program includes instructions that make the processor perform the steps that implement the methods described here. The program can be implemented as a single module or as a plurality of modules that operate in cooperation with each other. The program is contemplated to represent a software component that can be used in connection with an embodiment of the invention.
[0108] A communication subsystem 1125 is provided to communicate information from the microprocessor 1120 to the user interface 1110, such as an external device (eg, handheld or a computer that is connected in a network to the communication subsystem. communication 1125). Information can be communicated by the 1125 communication subsystem in a variety of modes, including Bluetooth, WiFi, WiMax, RF transmission, and so on. A number of different network topologies can be used in a conventional way, such as wired, optical, 3G, 4G, and so on.
[0109] The communication subsystem may be part of an electronic communicative device including, by way of example, a smartphone or cell phone, a personal digital assistant (PDA), netbook, laptop computer, and so on. For example, the communication subsystem 1125 can be directly connected through a device such as a smartphone such as an iPhone, Google Android Phone, BlackBerry, Microsoft Windows Mobile enabled phone, etc., or a device , such as a heart rate or blood pressure monitor (such as that manufactured by Withings SAS), weight measuring scales (such as those manufactured by Withings SAS), exercise equipment, or the like. In each instance, each of the devices comprises or interfaces with a module or unit for communicating with subsystem 1125 to allow information and control signals to flow between subsystem 1125 and external user interface device 1110. The communication device can cooperate with a conventional communicative device, or it can be part of a device that is dedicated to the purpose of communicating information processed by the microcontroller 1120.
[0110] When an electronic communicative device, such as the types noted above, is used as an external 1110 user interface device, the display, processor and memory of such devices can be used to process health-related information with the purpose of providing a numerical assessment. Otherwise, system 1100 may include a display 1140 and a memory 1150 that are associated with the external device and used to support data communication in real-time or otherwise. More generally, the 1100 system includes a user interface that can be implemented, in part, by software modules running on the 1120 microcontroller processor or under the control of the 1130 external device. output such as a display (eg the 1140 display).
[0111] Biosensors 1115 can be used to directly collect health information about a user and report that information. The biosensor can be placed in contact with the user's body to measure the user's vital signs or other health related information. For example, the biosensor can be a pulse meter that is worn by the user in contact with the user's body so that the user's pulse can be monitored, a heart rate monitor, an EKG device, a pedometer, a monitor blood glucose or one of many other devices or systems. The biosensor can include a communication module (e.g. communication subsystem 1125) so that the biosensor can communicate, wired or wireless, the monitored data. The biosensor can communicate the monitored data to the user interface device, which in turn communicates this information to the microcontroller. Optionally, the biosensor can directly communicate the monitored data to the microprocessor. The use of biosensors provides a degree of reliability in the reported data, as it eliminates the user error associated with manually self-reported data.
[0112] Alternatively or in addition, the user can self-report their related health information by manually entering the data. Thus, in another deployment, as shown in Figure 4A, a person's health related data is entered directly into an 1160 computer and provided over an 1170 network to an 1180 server computer. (All computers described here have at least a processor and a memory.)
[0113] Regardless of implementation, the system provides a means to assign a numerical value that represents an individual's relative health. The numerical value is described here as a “health score” and can be used to assess an individual's health based on health-related information collected from a user. The health score is calculated based on the collected health information using an algorithm. The user or communication subsystem 1125 provides the system with health-related information pertaining to a number of health parameters. Pre-determined weighing factors are used to designate a relative value of each of the parameters that are used to calculate the health score. The user's health score is then calculated by combining the weighted parameters in accordance with an algorithm. For example, the parameters might be a person's blood glucose level and body weight. An “a” weighting factor is applied to blood glucose data and a “b” weighting factor can be applied to body weight data. If blood glucose data is a more important factor in determining a person's health than body weight, then the “a” weighting factor will be greater than the “b” weighting factor so the glucose data have a greater impact on the calculated health score (eg, Health Score = Glucose*a + (Weight/100)*b). In certain deployments, the weighting factor is a non-unit value (eg, greater than or less than one, but not one). Minor or additional factors may be included in the calculation of the health score, and an offset amount may be included that is added or subtracted or that modifies the entire calculation, in certain deployments, such as to account for age or gender as two reasons possible; however, the foregoing is intended as a non-limiting example of how to calculate a health score. Other parameters that can be measured and included in the calculation include measurements of blood pressure, height, body mass index, fat mass, medical conditions such as diabetes, ventricular hypertrophy, hypertension, irregular heartbeat, and fasting glucose values . Where absent, a parameter may be omitted from the calculation or may be estimated from other parameters and/or values obtained from a sample group of individuals having similar parameters.
[0114] In addition to the intrinsic medical parameters, a user's physical activity is also considered when calculating their health score. Physical activity can be monitored via an appropriate activity-dependent sensor. Sensors can include a GPS unit, an altimeter, a depth gauge, a pedometer, a cadence sensor, a speed sensor, a heart rate monitor, or the like. In the case of gym-based activities, computerized exercise equipment can be configured to provide data directly into the user-completed program (for example, a so-called elliptical trainer/crossover can provide far better data on training than a user pedometer, etc.). While automated capturing of parameters pertaining to a user's physical activity is preferred, a user interface for manual activity entry is also provided. In this regard, an exercise machine such as a treadmill, elliptical machine, exercise bike or weight lifter with a rack of weights or bands may be provided with a communications interface to communicate with the system described herein for to provide the extrinsic physical activity parameters to the system and to receive and further include a processor configured to process data from the system to automatically adjust an exercise program on the exercise machine to meet a goal, challenge, or other objective for that user . Lifestyle data such as diet, smoking, alcohol consumed and the like can also be collected and used when calculating the health score. In one embodiment, a barcode or RFID scanner can be used by a user to capture data about consumed foodstuffs which is then translated into a remote system such as server 1180 or a website in communication with server 1180, in parameters such as daily calorie, fat and salt consumption. In part, the system relies on such data being provided by the user while other data can be obtained through data network connections once connectivity rights and permissions are in place.
[0115] Physical activity and lifestyle data are tracked over time and a decay algorithm is applied when calculating its effect on the health score, as discussed in more detail below. As such, physical activity in the distant past had a reduced positive effect on the health score. Preferably, the weighing factors used in the algorithm for computing the health score are adjusted over time in accordance with a decay component that is arranged to reduce the relative weight of the parameters that are used in the calculation. The decay component can itself comprise a weighting value, but it can also comprise an equation that considers at least one factor specifically associated with the user, such as the user's weight or weight variation, age or age variation, any medical conditions known by the system, and any of the other parameters that may be known by the system, or a curve that is set up considering these factors such that a value can be read from the curve as a function of values along the axes for such user. In this way, the decay component can reduce the relative weight of the parameters used to calculate the health score for a first user differently than for another user, such as when the first user has a first age or age range and the second user has a second age or age range.
[0116] A central system, preferably a database and website that can be hosted, for example, by server 1180, maintains data about each user and their health score and associated parameters and their trends over time. Data may be maintained in such a way that sensitive data is stored independent of human identities as understood in the art.
[0117] The calculated health score for each user is then processed in dependence on a system, group or user profile in the central system. Depending on the profile settings, the health score and associated trends can cause various automated actions. For example, this can cause: triggering of an automated alert; providing user feedback such as daily email updates; triggering automated motivation communication, prompts, and/or selected goal setting to alleviate a perceived issue; setting up a training program; or automated referral for medical analysis.
[0118] The user's health score is also provided to a designated group of recipients via a communication portal. The recipient group can comprise other selected users of the system (eg, friends and family) so that the health scores of the other selected users can be compared against the health scores of still others. In alternative arrangements, all users can see the scores of other users, or recipient group can be defined as a specific health insurance provider so that price quotes can be provided to the individual. Other possibilities are within the scope of the invention.
[0119] With reference now to Figure. 5, a schematic flowchart in accordance with an embodiment of the invention is depicted in support of an assessment of a person (e.g., a patient or user) to provide a health score. In step 1210, the user starts the process for collecting, processing and publishing the related health data. For example, a person using a mobile electronic device (eg, a smartphone or handheld computing device) selects the software application, which starts the program running on the device's processor, or the user can access a page based on Internet where code is executed on a remote processor and served to the user's local device. An identification module asks the user to identify themselves and authenticate their identity. This can be done by prompting the user to enter a username and password, or by other means such as fingerprint reader, security key, encryption or other mechanism to ensure the user's identity. Alternatively, if the user is accessing the system via a personal electronic device, the identification data can be stored in the local device's memory and automatically accessed for the purpose of confirming the user's identity.
[0120] At step 1220, a data collection module running on the processor may prompt the user to provide health related data corresponding to a number of parameters. In a deployment, one or more parameters are automatically provided by the 1125 communication subsystem. The parameters can include the user's body weight, height, age, and fitness activity information. Such measurable medical parameters are intrinsic parameters of the user. The user's body weight and height provide information about the user's current health status. Gym activity information corresponds to the amount of exercise the user engages in. This information is an example of a physical activity parameter that is an extrinsic user parameter. For example, the user can enter information about their daily gym activities, such as the amount of time the user has engaged in physical activity and the type of physical activity. If the user went to the gym and exercised on a bicycle for thirty minutes, for example, this information is entered into the system. The user's fitness activity information provides information about the actions being taken by the user to improve their fitness.
[0121] A user's body weight, height, age, and fitness activity information are just some of the parameters for which information can be collected. The system can collect and process a multitude of other parameters that can be indicative of a user's health. For example, parameters can include blood glucose levels, blood pressure, blood chemistry data (eg hormone levels, essential vitamin and mineral levels, etc.), cholesterol levels, immunization data, pulse, blood oxygen content, information regarding the food consumed (eg, calorie, fat, fiber, sodium content), body temperature, which are just a few of the few possible non-limiting examples of parameters that can be collected. Several other parameters that are indicative of a person's health that can be reliably measured could be used to calculate a person's health score.
[0122] The collected health parameter information is stored in a memory in step 1230. In step 1240, a weight module remembers weighing factors from memory. Weighing factors can be multiplication coefficients that are used to increase or decrease the relative value of each of the health parameters. A weighting factor is assigned to each health parameter as shown in the gift formulas. Weighing factors are used to control the reactive values of health parameters. Some health parameters are more important than others when calculating the user's health score. Correspondingly, weighing factors are applied to health parameters to increase or decrease the relative effect that each factor has in calculating the user's health score. For example, a user's current body weight may be more important than the amount of physical activity the user engages in. In this example, the body weight parameter would be weighted more heavily by assigning a higher weighting factor to that parameter. In step 1250, the weighting module applies the recalled weighing factors to the collected health parameter values to provide the weighted health parameter values. The weighting factor can be zero, in which case a specific parameter has no impact on the health score. The weighting factor can be a negative value for use in some algorithms.
[0123] After the parameters have been weighted, the user's health score is computed in step 1260 via a score module operating on the processor. The scoring module combines the weighted parameters according to an algorithm. In one deployment, the health score is the mean of the user's body mass index (BMI), health score, and the user's physical health score minus twice the number of years a person is younger than 95. The algorithm formula for this example is reproduced below: Health Score = ((BMI Health Score + Physical Health Score)/2) - 2*(95-Age).
[0124] The user's BMI Health Score is a value between 0 and 1000. The BMI Health Score is based on the user's BMI, which is calculated based on the user's height and weight, and how far the user's BMI deviates of what is considered a healthy BMI. A graph or formula can be used to normalize the user's BMI information so that dissimilar information can be combined. A target BMI value is selected which is assigned a maximum point value (eg 1000). The more the user's BMI deviates from the target value, the less points are awarded. The user's Physical Health Score is based on a person's physical activity or exercise. In one realization, it is the sum of the number of hours of exercise (ie, the amount of time the user has engaged in physical activity) in the last 365 days where each hour is linearly passed over time, so less activity recent is rated less. The resulting sum is multiplied by two and is limited to 1000. This normalizes the gym information so that it can be combined to arrive at the health score. A target daily average of physical activity is selected and awarded with the maximum amount of points (eg 1000). The user receives fewer points based on how much less exercise he has engaged in compared to the goal.
[0125] In another implementation, the health score is determined from a number of subscores that are kept in parallel in addition to the BMI health score and Physical Health Score. Likewise, the health score can be determined using similar information in a combined algorithm as discussed above using different adjustments or no age adjustments.
[0126] Intrinsic medical parameters are processed to determine a baseline health score. Extrinsic parameters, such as those from exercise, are processed to determine a value that is allocated to a health cluster and a bonus cluster. The value, preferably expressed in MET hours, associated with a physical activity is added to both the health grouping and the bonus grouping. A daily decay factor is applied to the bonus pool. Any excess decay that cannot be accommodated by the bonus pool is then deducted from the health pool. The amount of decay is determined dependent on the size of the health and bonus pool so that greater effort is required to maintain a high health and bonus pool. The health cluster value is processed in combination with the score from the intrinsic medical parameters for the purpose of calculating the overall health score value. This can be on a similar basis to the implementation described above, or it can include different parameters and weighing factors. In one embodiment, the health cluster value is a logarithm or other statistical function is applied to age the respective values over time so that only the most recent activity is counted as being fully effective for the health/bonus cluster. An exemplary user interface showing the health score, health pool and other selected measured parameters (as it will be appreciated that many simply combine to compose the scores) is shown in Figures 6a and 6b. Several subscores and their trends are recorded, as shown in Figure 6c.
[0127] As will be appreciated, MET hours are kcal expended divided by kilograms of body weight, i.e., 100 kcal expended by a 50 kg person is 2h MET. This is "normalized energy", making the system fair to people of all weights. With this method, the clusters can be the same size for each per person as energy is normalized for the person based on their body weight.
[0128] In a deployment, each person is assigned a health cluster having a capacity of 300h MET and a bonus cluster having a capacity of 60h MET.
[0129] When someone performs activity A, the groupings are updated as follows: H = min(H + A * alpha, 300) B = min(B + A * (1 - alpha), 60)
[0130] Where H is the health cluster score, B is the bonus cluster score, A is the h MET value for activity, and alpha is a contesting broad system (selected between 0 and 1) that determines the proportion in which the activity contributes to the respective groupings.
[0131] Activity is split between health grouping and bonus grouping. Any excess h MET activity above the upper limit of any cluster is discarded. A daily decay value D is applied to the clusters as follows: D = f(H, B) B = B — D If B < 0: D = D + B B = 0 If D < 0: D= 0
[0132] The decay is fully applied to the bonus pool, and if the bonus pool is empty, the rest is applied to the health pool. In this realization, no cluster ever drops below zero.
[0133] The system finds its equilibrium where A is equal to f(H, B), i.e., when the average daily activity matches the average daily decay. The function f(H, B) is highly nonlinear with respect to H and B. In essence, it has less sublinear effort to maintain a small cluster, and more superlinear effort to maintain a large cluster. This is to make sure the average person can maintain a, say, a half full health cluster (150, corresponding to a score of 500), whereas they need massively superior effort (typically only delivered by an endurance athlete professional) to maintain a total health cluster (300, corresponding to a score of 1000). Figure 6f shows a simulation of buffer clustering and health reservoir score over time assuming activity ranging between 11.5 and 16 h MET per day and 2 rest days per week. A perfect health score of 1000 would require 30h MET activity per day, as seen from the curve in the upper right corner of Figure 6f.
[0134] Preferably, the health score is based on a weighted combination of the health factor(s) and the person's exercise record over time. Health factors can be updated regularly by the user. For example, the user can provide related health information after each event that is tracked and processed by the system. User can refresh after a meal, after exercising, after weighing, etc. In case an activity/event is recorded by a sensor, handheld device or similar, the captured/calculated parameters can be automatically uploaded and used to produce a revised health score. For example, feedback could be provided showing the effect of exercise while a user is running, exercising on exercise equipment, etc. On selected achievements, feedback can be provided to an administrator, such as a member of the academy team where it is determined that a user is exceeding a predetermined threshold (which, due to knowledge of their health, can be varied with regarding your health score or other recorded data). Correspondingly, health related data can be updated in a near real-time manner.
[0135] User can also update information twice a day, once a day or at other periodic times. Furthermore, the health score can be based on an average of the information over time. Physical activity, for example, can be averaged over a period of time (eg, over a week, month, or year). Averaging data over time will reduce the impact to the health score caused by fluctuations in the data. Periods when data were uncharacteristically high (eg, the person was engaging in a large amount of physical activity in a short period of time) or uncharacteristically low (eg, a a person has not engaged in any physical activity for a week due to illness) do not dramatically affect the average health score over time. Related health information can be stored in memory or in a database accessible by the processor.
[0136] Stored data can also be used to predict future health scores for a user. A prediction module can analyze past data (eg, fitness habits, eating habits, etc.) to extrapolate a predicted health score based on an assumption that the user will continue to act in a verifiable manner. For example, if the data demonstrates that a user has exercised one hour each day for the past thirty days, the prediction module can predict, in accordance with a prediction algorithm, that the user will continue to exercise one hour each. one of the next three days. Correspondingly, the scoring module can calculate a predicted health score at the end of the next three days based on information from the prediction module. It can also factor the prediction into other actions. For example, the system might suggest a level of physical activity with more effort or challenge for someone who has a high health score but is predicted based on past experience and then takes countless days off for recovery. Furthermore, the system can provide encouragement to the user to maintain a course of activity or modify behavior. For example, the system can send a message to the user indicating that if the user increased physical activity by a certain amount of time, the health score would go up by a certain amount. This would allow the user to set goals to improve health.
[0137] The use of the health score allows for a relative comparison of a user's health with that of another person, although each person may have very different characteristics, which would make a direct comparison difficult. For example, a first user (User 1) may have a very different body composition or engage in very different physical activities as compared to a second user (User 2), which makes direct comparison of each user's relative health difficult . The use of the health score makes the comparison of two users possible with relative ease. In one example, User 1 is slightly overweight, which would tend to reduce User 1's health score. However, User 1 also engages in large amounts of physical activity, thus increasing the user's overall health score. In contrast, User 2 has an ideal body weight, which would contribute to a high health score, but engages in very little physical activity, thus reducing the health score. User 1 and User 2 are very different in terms of their health related parameters. Correspondingly, it would be very difficult to assess and compare the relative health of User 1 and User 2. In accordance with the invention, information related to certain health parameters is collected from User 1 and User 2, which are used to calculate a general health score. A comparison of User 1 and User 2 health scores allows for an easy assessment and comparison of the health of these two users, even though they are very different and have very different habits. Therefore, the health score has significant value so that members of a group can compare their relative health and so that other entities (eg, employers, health care insurers) can assess an individual's health. . Examples are shown in Figures 6d and 6e where tabular (current) and graphical (historical, current and forecast) scores of different users are shown. As can be seen in Figure 6e, Katrin is expected to outperform the user (Andre) soon, unless he improves his lifestyle and performance. In Figure 6d, the impact of the decay algorithm is illustrated to show the effect on the health score of a given user (Andre') and the people he identified as friends. As noted, user Andre has a current health score of 669 which places this user between friends Irene (health score 670) and Helle (health score 668). The decay algorithm acted on all health scores shown in the screenshot of Figure 6d, as indicated in the column “Δ Day 1”. More specifically, most of Andre's friends had their health score reduced by 1 point due to "no activity." A lack of input to the system is a basis for the processor to run the decay algorithm to determine a status of “no activity” for a given user. The effect of a day of this status according to the decay algorithm illustrated for most users is a reduction of 1 point in one day, and a reduction of 5 points in the course of a week. As such, the decay algorithm has a non-linear funneled impact on an overall health score.
[0138] As illustrated, user Andre had moderate activity recorded in a memory that is accessible to the system. As a result, moderate activity is processed and results in a one-day change (delta) that is positive, and a change that acts against the influence of the decay algorithm. Consequently, Andre will be able to observe, as well as friends who have had access to his published health score, that he has increased his score from 667 to 669 in one day, and from 662 to its present value in the last seven years as a result of “ moderate activity.” Furthermore, a prediction is computed using the underlying algorithm and a data extrapolation based on the most recent reasons (ie incoming data) to increase another 5 points. On the other hand, due to low activity but good diet, Helle in the same period of time lost 1 point on the last day and a total of 1 point on the last 7 days and is predicted to lose another point if this rate continues. As such, Helle receives feedback from running the algorithm and outputs provided by the system that can encourage more activity. On the other hand, Irene has no activity and a poor diet which results in a more aggressive alteration to her health score and predicted and historical impact with longer view on her score. Again, this feedback, which can be provided to users and their friends or members of a user group who have joined for a challenge, etc., to provide individual or team motivation in order to engage in physical activity, eat well. and etc.
[0139] Furthermore, the health score provides an indication of the individual's relative health without revealing the underlying data used to calculate the health score, which can be sensitive information. For example, a user may be uncomfortable revealing their weight, age, or the amount of time they spend exercising with other people or entities. People may be embarrassed to share their weight or the fact that they virtually never go to the gym. However, since the health score is derived from several factors, the underlying data used to calculate the score is kept private. This feature will facilitate the sharing of the user's overall health, as users will not have to disclose private data about them. For example, a person may be slightly overweight, but he or she often goes to the gym. Correspondingly, such a person may receive a relatively good health score. While a person may not want to disclose their weight, they can still disclose their health score which conveys information about their relative health without disclosing the underlying details. Intrinsic medical parameters (eg, weight, height, etc.) and extrinsic physical activity parameters (eg, exercise duration, frequency, intensity, etc.) are transformed into a masked composite numeric value. The masked numeric value is published while the information collected regarding intrinsic medical parameters and extrinsic physical activity parameters is kept private. Underlying intrinsic medical parameters and extrinsic physical activity parameters are protected so that a third party is not able to determine such parameters based on the health score number. This is because the parameters can vary in many different ways and yet the health score number could be the same (eg, a heavier person who exercises frequently may have the same health score as a person who is not overweight, but do not exercise as often). Thus, having the health score alone does not reveal a person's health related parameters. Correspondingly, the underlying health statistics are masked, yet the health score can be used as a reference point to indicate a person's health for a variety of applications.
[0140] After the score module calculates the user's health score, in step 1270, a publishing module remembers from memory the designated group of recipients who are authorized to receive the health score. The recipient group can be the user's friends or family, sports team members, employers, insurers, etc. At step 1280, the publishing module causes the health score to be published to a group of friends, the information can be published to an internet-based social networking portal where access to the data is limited to such designated members of the group.
[0141] Health parameter data and health scores can be stored over time in a memory or other database so that a user can track their progress. Graphs can be generated for a user to track progress and analyze where there might be improvement in behavior. Furthermore, trends can be identified that can lead to the diagnosis of medical problems and/or eating habits. For example, if a person's weight is continuing to increase despite the same amount or increased amount of physical activity, the system may trigger or suggest that he or she can seek certain medical tests (eg, a thyroid test, thyroid test pregnancy) to determine the cause of weight gain.
[0142] In certain deployments, the majority of the system is hosted remotely from the user and the user accesses the system via a local user interface device. For example, the system can be internet-based and the user interacts with a local user interface device (eg, personal computer or mobile electronic device) that is connected to the internet (eg, via a network wired/wireless communication device) to communicate the data with the internet-based system. The user uses the local interface device to access the internet based system where the memory and software modules are operating remotely and communicating over the internet with the local device. The local device is used to communicate data to the remote processor and memory, where the data is remotely stored, processed, transformed into a health score and then provided to designated groups via a restricted-access internet portal. Alternatively, the system can be primarily deployed via a local device where data is locally stored, processed and transformed into a health score, which is then communicated to a data sharing portal for remote publication to designated groups.
[0143] The system can be deployed in the form of a social network structure that is executed by the software modules stored in memory and operating on the processors. The system can be deployed as a separate stand-alone “health-themed” social networking system or as an application that is integrated with an existing social networking system (eg, Facebook, MySpace, etc.). The user receives a home page where the user can enter information, manage what information is published to the designated groups, and manage the membership of the designated groups. The home page includes user commands to enter health related information for each of the various parameters. The user can enter their weight, date of birth, height, physical activity and other related health information. The user's health score is then calculated. The health score is shared with other users who are designated as part of an allowed group to have access to such information. Furthermore, the user can see the health score information of others in the group. Correspondingly, the user is able to compare their overall health with the health of others in the group. Comparing health scores with others in the group can provide motivation for individuals in the group to compete to improve their health scores. Other information, such as health tips, medical news, drug information, local fitness events, health services, advertising and discounts for medical and/or related fitness supplies and service, issuance of fitness challenges or related fitness goals. health, for example, can be provided via the home page.
[0144] In additional deployments, the health score can be a composite of a Health Metric Model score and a Quality of Life Model score. Combining scores from multiple models provides a more holistic assessment of a user's health. The Health Metric Model score assesses a user's health based on relatively easy quantifiable parameters (eg, age, sex, weight, etc.) and compares those numbers to acceptable population study models. The Quality of Life Model score focuses on a self-rated measure of a user's quality of life based on the responses to a questionnaire (ie, the system considers the user's own assessment of their health and quality of life), as there are correlations between how an individual “feels” about their life and a realistic measure of health. A combination of scores from these two models, which will be discussed in more detail below, provides a more inclusive and holistic assessment of health.
[0145] The Health Metric Model score is based on a user's medical parameter information, such as their medical history information, attributes, physiological metrics, and lifestyle information to the system. For example, the system can provide the user with a quiz to request responses (yes/no, multiple choice, numeric entry, etc.) or provide the user with form fields to fill out. Medical history information may include the user's history of medical conditions and/or prevalence of medical conditions in the user's family. Examples of medical history information may include information such as whether the user has diabetes, has direct family members with diabetes, whether the user or family members have a history of heart attack, angina, stroke, or Transient Ischemic Attack, a history of atrial fibrillation or irregular heartbeat, if the user or family members have high blood pressure requiring treatment, if the user or family members have hypothyroidism, rheumatoid arthritis, chronic liver disease, liver failure, left ventricular hypertrophy, congestive heart failure, regular use of steroid pills, etc.
[0146] The Health Metric Model score can also be based on user attributes. Attributes can include age, gender, ethnicity, height, weight, waist size, etc. In addition, the Health Metric Model score can be based on the user's physiological metrics. Examples of physiological metrics may include systolic blood pressure, serum total cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL), triglycerides, high sensitivity C-reactive protection, fasting blood glucose, etc. Entries can also include parameters from a user's lifestyle. For example, lifestyle parameters can include entries on whether the user is a smoker (has smoked, currently smokes, smoking levels, etc.), how much exercise the user does (frequency, intensity, type, etc.), type of diet (vegetarian, high protein diet, low fat diet, high fiber diet, fast food, restaurant, home cooking, processed and prepackaged foods, meal size, meal frequency, etc.). These are some of the examples of parameters that can be used to compare a user's health indicators to survival probability models for the purpose of calculating the user's Metric Health Model score.
[0147] Probability of survival prediction models can be used to predict the probability that an individual will experience one or more serious health events in a given period of time. Mathematical models can estimate this probability from observed population characteristics. Using observational data on a set of unambiguous serious health events, such as stroke or heart attack, models can generate the probability that an individual will experience such an event in a given time horizon from a set of marker measurements, or predictors, for the event (eg, information about a user's medical history, attributes, physiological metrics, lifestyle, etc., as described above). The time distance between when predictors are measured, and the target event that is generated by such models, is termed a probability of survival, although it must be understood that not all target events considered are necessarily fatal.
[0148] These survival probability models are typically derived from the study of generally large populations that are followed for a considerable length of time, typically more than ten years, and the statistics collected on the observation of the event(s)( s) targets are summarized and generalized using mathematical methods. There are a number of such models that exist that have been extensively validated and maintained and improved by periodically updating model parameters using the new data. Examples of existing models may include a subset of models developed and maintained by the Framingham Heart Study (an extensive bibliography of results obtained from the Framingham Heart Study is available at www.framinghamheartstudy.org/biblio), a subset of the models developed and maintained by the University of Nottingham and the QResearch Organization (see, eg, J Hippisley-Cox et al, Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2, BMJ 336 : 1475 doi: 10.1136/bmj.39609.449676 .25 (Posted 23 June 2008)), the ASSIGN model developed by the University of Dundee (see, for example, H Tunstall-Pedoe et al, Comparison of prediction by 27 different factors of coronary heart disease and death in men and women from the Scottish Heart Health Study: cohort study; BMJ 1998;316:1881), the Reynolds model (see, for example, PM Ridker et al, C-Reactive Protein and Parental History Improves Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men, Circulation 2008; 118;2243-2251, and Development and Validation of Improved Algorithms for the Assessment of Global Cardiovascular Risk in Women, JAMA, February 14, 2007—Vol. 297, No. 6), the PROCAM model from the Münster Heart Study (see, for example, Simple Scoring Scheme for Calculating the Risk of Acute Coronary Events Based on the 10-Year Follow-up of the Münster Prospective Cardiovascular Study (PROCAM) , Circulation. 2002;105:310-315), and the SCORE model (see, for example, RM Conroy et al, Ten-year risk estimate of fatal cardiovascular disease in Europe: the SCORE project, European Heart Journal (2003) 24, 987-1003). Other constituent risk models can also be included. In addition, precursor models can also be used. Precursor models predict the development of a first condition (eg, high blood pressure), where the development of the first condition is predictive of developing a second condition (eg, heart disease). There are models that generate estimates of the probability of developing diabetes or high blood pressure, for example, which are the two important predictors of mortality. A high probability of developing diabetes within five years, for example, will independently increase the probability of a serious cardiovascular event within the next ten years. Several such precursor models can be included and the inclusion of the precursor models leads to more accurate metric risk models, but more importantly, it also leads to possible mortality risk reduction through well-defined modifiable aspects of lifestyle.
[0149] Traditional survival probability models have certain inherent limitations that result from the procedures used to build them. In deriving such models, researchers compromised between accuracy and usability. It is difficult for an inductive model, meaning a model derived directly from the data, to include all possible predictors. This is partly because not all relevant predictors of a specific event are known, but also partly because some known predictors can be difficult or expensive to measure. In fact, several well-known risk markers, such as generic factors, are often not included in such models. Therefore, several known potential and predictive metrics can be excluded as covariates when deriving a given survival model.
[0150] Survival probability models are built using data collected from a given population, and thus summarize and generalize the morbidity and mortality characteristics of the studied population. However, such a model could be in variance when compared to risk estimates derived from other populations. When a given model is used in a population that differs from the one in which the model was built, it often underestimates or overestimates a specific risk, as only some predictors are often considered, and due to other relevant predictors that cannot be included in the model could very well differ between two populations.
[0151] Considering the above discussion, along with basic probabilistic logic, a judicious combination of derived models for several different populations will generate a better view of the risks that a randomly selected individual is exposed to, and thus will be more robust in estimating risks for the population as a whole. Furthermore, based on mathematical foundations, under very general assumptions, certain methods of model combination, called predictor regulation, can improve the accuracy of the constituent models. In fact, tuning a set of models, when done correctly, will produce a model with accuracy that is, in the worst case, equal to that of the most accurate model in the regulated set.
[0152] Correspondingly, the Health Metric Model score can be calculated by comparing the user's medical parameter information to the survival probability models. A score, preferably in the range of 0 to 1000, with the top end meaning perfect health and the bottom end meaning poor health, can be derived after a two-step process. First, an overall survival probability is obtained from a combination of survival probabilities generated by individual survival probability models, as described above. Second, the resulting probability of survival, which is a number in the range 0 to 1, is transformed using a nonlinear parametric mapping function in the range 0 to 1000. The parametric mapping function is adjusted so that it is linear, with a high slope, in the region of typical survival probabilities, and asymptomatic slopes outside the low and high ends of the survival probability distribution. The mapping function is designed to be strongly reactive to changes in the typical survival probability region.
[0153] As discussed above, the health score can be composed of the Health Metric Model score, and also the Quality of Life Model score. The Quality of Life Model score is based on a user's responses to a set of questionnaires. The system can include several different quizzes with some common questions. The type of questionnaires and the type of questions presented to the user there can be tailored based on a user's health parameters (i.e., age of the user, other data in the user's medical history, etc.). A specific questionnaire can be generated and presented to the user based on information about the user that is known by the system. Questions can be presented with an appropriate multiple choice answer that the user can check/check off on a form, with no free-form text being entered by the user to allow for easier evaluation of the answers. Other types of responses are possible (eg, rate how true a statement is to user 1-10). The following list provides several sample questions (in no specific order) on a number of health-related quality of life topics that can be used in a system quiz. Sample Questions: How do you rate your quality of life How do you rate your overall health How much do you enjoy life To what extent do you feel your life is meaningful How well are you able to concentrate How safe do you feel in your daily life How healthy is your physical environment Are you satisfied with your appearance To what extent do you have the opportunity for leisure activities Is recurrent medical treatment essential for your quality of life How long were your activities limited due to your major disability or health problem Do you need help to address your personal care needs due to health problems Do you need help to deal with your routine needs due to your health problems Are you in any way limited in any activities due to any major disability or health problem How true or false is each of the following statements for you : I seem to get sick a little easier than other people I am as healthy as anyone I know I suppose my health gets worse My health is excellent You suffer from any of the following disabilities Main or health problem that limits your activities : Arthritis or rheumatism Spine or neck problem Cancer Depression, anxiety or any emotional problem Vision problem Fractures, bone/joint damage Hearing problem Respiratory problem Walking problem Other disability or problem During last 30 days, for how many days: your physical health was not good Has your pain made it difficult for you to do your usual activities, such as self-care, work, or recreation did you feel sad, melancholy or depressed did you feel worried, tense or anxious did you feel like you didn't get enough rest or sleep did you feel very healthy and full of energy are you a very nervous person Did you feel so depressed that nothing could cheer you up Did you feel calm and peaceful did you have enough energy did you feel discouraged and upset did you feel exhausted Are you a happy person did you feel tired How satisfied are you with: your sleep your ability to carry out your daily activities your ability to work yourself your personal relationships your sex life what support do you get from your friends the conditions of your place of residence your access to health services your transport Are you limited in any of the following activities due to your health : Vigorous activities such as running, lifting heavy objects, participating in strenuous sports Moderate activities such as moving a table, pushing a vacuum cleaner, bowling or play golf Lift or carry grocery shopping Climb several flights of stairs Climb one flight of stairs Bending, kneeling or bending Walk more than 2 kilometers Walk several blocks Walk a block Bathe or dress
[0154] This list above is just a sample of questions that can be presented to a user. User responses to questions are given a value. For example, each of the multiple choice answers can be assigned a specific value, and all user answers can be calculated to generate a score. Furthermore, different questions and different answers can be weighted differently, as some questions, or the severity of the answer, can have a greater predictor of the user's health. The system can also assign a value based on the user's response to a combination of questions, as certain combinations can be more predictive of health. Correspondingly, by evaluating user responses to the questionnaire, a Quality of Life Model score can be derived. Preferably, the Quality of Life Model score is a numerical value ranging from 0 to 1000.
[0155] The health score is computed as a weighted average of the Health Metric Model score and the Quality of Life Model score. The health score can be presented to the user. The health score can be presented as a numerical value, as a graphical value (i.e., as a gauge, bar, or cursor), or a combination of both, for example. Referring to Figure 6A, the health score is presented by a combination of a numeric score 1302 and a cursor 1304. The cursor can also be color-coded to indicate the score. The position of the cursor bar 1306 indicates the user's score.
[0156] An advantage of presenting the health score is that it is not necessary to present the survival probabilities and raw metrics to the user. Instead, users are presented with a standardized score. Preferably, this is true of the overall Metric Health Model and Quality of Life scores, but it is also true of the relevant model inputs. This is primarily done to standardize all output, in the sense that users do not need to know whether high values for a particular input variable are good or bad; in all cases, high scores for any input value lead to higher health score values, and low input variable scores lead to lower overall health score values.
[0157] Furthermore, another advantage of standardized health scores is that users can compare health scores against other users. This allows for comparative benchmarking (against friends, co-workers, etc.) with other users. Such score comparisons can be part of a gaming component of the system where the user competes against other users, as will be described in more detail below. Game aspects of the system can be used to motivate the user of the health scoring system, such as a comparison of scores between selected user groups, comparison of individual scores within configurable subpopulation distributions, time tracking of scores and setting goals, among others. Referring to Figure 6B, the numerical user score 1302 and graphic score 1306 are presented in combination with a range of scores 1308 from a group (eg the world) so that the user can see their score in comparison to others in the group. Gaming incentives can be extended by users to allow comparison of health scores among users that may differ substantially in one or more of several specific input parameters, such as age, weight, and prior risk conditions. The system highlights improvements in user modifiable metrics, specifically lifestyle components, and these score improvements provide incentives to the user. This allows for fair competition between users of a parent and their children, for example, via the health score. In one aspect, the health score provides equalization among users of different characteristics and is, therefore, similar to that of a disabled person in some sports. Referring to Figure 6C, user's score 1306 is compared to scores 1310a-e for a user's selected group of friends. Referring to Figure 6D, the user's individual medical parameters (eg, medical data provided as a part of the Metric Health Model) can be compared against other users graphically without revealing the actual underlying values. High-density lipoprotein (HDL) level, low-density lipoprotein (LDL) level, systolic blood pressure (sBP), diastolic blood pressure (dBP), body mass index (BMI) and fasting blood glucose level ( fBG) are shown in a graph 1312. User scores are represented by a line 1314, user friend scores are each represented by a different point 1316, and a distribution block 1318 for a larger population group (p. eg Switzerland) is also shown. In this way, the user can compare their individual parameters to a group of friends and the average to a large population group.
[0158] Users can enter data into the system at the time of an event (i.e., exercise event, food consumption, blood pressure measurement, etc.), and see the resulting update of their health score in real time. The system can include expandability capabilities, allowing users to view the various health score component scores, including tracking over time and corresponding trends across all scores; it also includes setting the goals in the various scores.
[0159] As an example of using the system, upon registration with the system (eg, initial use of the system), a user is asked to provide medical history data. The user is also asked to complete a complete Quality of Life questionnaire selected by the system for a given user based on the user's medical history and parameters provided by the user. After registration, at periodic intervals, users are presented with short subsets (3-5 questions) of their personalized Quality of Life questionnaire to keep their answers up-to-date and track changes. Users can enter entries for the Health Metric Template at any time, and the system prompts the user for values that have not been updated for some time. Entries to the Metric Health Model can be acquired automatically by the system when accessing a series of digital measurement devices that have been integrated into the system (eg, the system can comprise a mobile electronic communication device, eg a smartphone, which is in wireless communication with a measuring device such as a blood glucose monitor so that parameters can be measured, transmitted and stored by the system). These can include weight, blood glucose, physical activity and other parameters. Various multifunctional digital measuring devices or devices can be included in the system. In the case of medical parameters that are more difficult to obtain with a home measuring device, such as serum lipid concentration levels, users are only asked to provide the relevant data once (system) the configured time period ( eg, annually and coinciding with a user's routine physical examination).
[0160] To avoid false scores, the system can include several algorithms to assess the validity of user inputs. Validation methods can range from those based on atypical detection to those based on multidimensional probability estimators. When the system detects a possible bad input value, it flags it and asks the user to confirm the value or enter a new one.
[0161] The system can generate all your scores, even when one or more entries are missing. It does this by entering the missing value or values using a variety of statistical methods ranging from those based on global population statistics, to methods based on the use of more complicated statistical models that are built on the platform. However, whenever entries include imputed values, the system clearly flags all affected scores, and periodically alerts the user to provide missing data. The system can also allow for scoring simulation, where the user can temporarily adjust their parameters so that a user can see how changing certain parameters (eg, losing weight) affects the user's score.
[0162] The system can also provide recommendations to users to take certain actions that can improve the user's health score. These recommendations can be very specific when any input variable is in your danger zone, and more generic when any input variable is outside its optimal range.
[0163] As discussed above, the health score can be used as a part of a game or competition aspect of the system. The game aspect increases the fun element of the system for the user and increases the user's affinity to continue using the system. The game aspect may be in the form of getting higher levels based on achievements, competing against others (eg in a league) and/or completing challenges. The “level” is a general indication of progress. The level can be monotonously increasing and will increase when getting activity points. Activity points can be earned by performing numerous activities, such as time spent performing physical activities (eg, exercise), improving someone's health score, improving someone's BMI, participating in system discussions (p .eg, the system may be an internet-based social networking platform and discussions or “classes” may be offered to teach the aptitude skills). A user's level can be displayed on a user's profile and in discussion posts so that other users can see the other's level. A user-level status can also provide access to specific items, system features and functionality, or rewards (eg, branded apparel).
[0164] Users can also compete within leagues in the system. Leagues are made up of user groups and users within the league can compete against each other (as part of a team or individually). Leagues can compete for a limited time (eg, monthly) and leagues can be assigned based on the level of users (using the user level as discussed above), the type of activity being carried out in the league and the region geographic location of users. For example, a specific league might be the “Greater Zurich Area” league of “mountain cycling” (sport) “bronze” (level) and a user's success in that league is measured by distance traveled and elevation climbed (quantity measure). In this way, bronze-level users living in the Greater Zurich Area who are interested in mountain biking can compete in this league. Limiting leagues to a specific region gives users something to relate to and all users can share in common, and even allows users to meet face to face (eg for group exercise events). One issue with a major international league is that such a league can seem anonymous, crowded and insignificant to some users (members competing against members residing in completely different continents with language barriers can inhibit group or team mentalities). Limiting leagues to level-specific parentheses equalizes the playing field for users of specific skill levels. Quantities to be measured to determine league performance can include distance (horizontal, vertical) and duration of physical activity performed, for example. Users can also form teams within leagues. Team leagues work in the same way as the leagues summarized above, however, ranking is based on overall team performance. The teams increase the communal aspect of participation in the activity. Teams can be fixed in size (eg 2, 3, 5, 10, etc., users).
[0165] Users can also be presented by the system with challenges to complete. Challenges can set a time frame for completing a goal. Challenge goals can be, for example, improvement of health score (normalized), completion of sport-related activity parameters (eg, total distance, total climb, etc.) or completion of a sport-related activity within a specific period of time (eg, completing a six-minute mile on a specific route). The challenge can be public and any user can join, or be limited to a group (eg friends, co-workers, social group, etc.). As an example, a specific public challenge might be an inline skating challenge in New York City for the route around the Central Park Loop measuring time to completion. Public challenges can be automatically generated by the system or by system administrators. Group challenges can be issued by group members. Challenges provide strong naming dynamics, encourage users to commit to exercise. Challenges (typically) have a lesser time commitment than leagues. Route selection can be automated with the community. In a first step, the community can publish routes on the system platform (eg a social network type website); in a second step, the system selects popular routes (i.e. routes with high user activity) as weekly challenges. Route validation is done by GPS tracking. Challenges can be sifted for safety to prevent the promotion of unduly risky challenging activities, such as dangerous mounting cycling descent routes.
[0166] League and challenge systems provide opportunities to award achievements. Achievement status indications can be collected and displayed on a user's profile. Achievements are much like a trophy, medal or award given to the user to complete challenges and/or succeed in a league activity. Many different achievements are possible, such as, related to the number of friends the user has in the system (community participation), achievements related to time, intensity and number of physical activities engaged (level of gym participation), achievements related to activities sport-specific (eg, distance covered), how often a user measures their parameters (eg, weight) for the purpose of keeping the system up to date, the amount of weight lost, or the ability to maintain the BMI , for example. The following list is an exemplary set of achievements and the activities required to obtain the achievements: Exemplary List of Achievements: Challenging: Participating in a public challenge. Successful Challenger: Participate in 10 public challenges. Champion: Win a challenge. Multiple Sport Champion: Win a public challenge in two different sports. International Challenger: Participate in a public challenge in two different countries. International Champion: Win a public challenge in two different countries. World Challenger: Participate in a public challenge on every continent. World Champion: Win a public challenge on every continent.
[0167] Other aspects of challenge and league systems are that systems can be linked to marketing opportunities. For example, merchants can patronize prizes for challenge winners. The award can be linked to the challenge (eg, gift certificate to the health food score for the weight loss challenge winner). In addition, challenge routes can be selected to instruct users to certain areas in order to increase tourism and start/end at selected destinations (eg bike challenge starts in front of the sporting equipment store).
[0168] One advantage of the system is that it provides users and user groups with benchmarking capabilities. It allows other groups, such as insurers or employers, to assess the relative health of individuals to determine each individual's health-related risks. Correspondingly, users can compare themselves against others to assess their comparative health level among a group of friends. Insurers can use health score information to set premiums for an individual or a group of individuals (eg, employees of a company). In other deployments, health scores may be provided to a group based on the health scores of individuals in the group. For example, a health score can be calculated for a company based on its employees so that an insurer can set premiums based on the company's health score compared to other companies. In additional applications, the health score can be used to assess the health of professional athletes in order to determine the athlete's true market value. Vast amounts of money and resources are invested in athletes at all levels in professional sports. A large component of the decision to invest in an athlete is based on the athlete's past performance. Other factors may include prior history of physical injury and the athlete undergoing a physical examination before the deal is completed. The health score can be used as an indicator of the athlete's current health and used as a predictor of the athlete's future performance. If the athlete's health score was low, it may indicate that the athlete is more likely to be injured or that physical performance will decline. Correspondingly, the health score can form a basis for a decision on whether to invest in an athlete. Health scores could also be used as a predictor of the outcome of a specific game played between two teams. For example, the health scores of individual team members can be aggregated to provide a team's health score. A comparison of team health scores may be indicative of the likely outcome of the game between the two teams (eg, the team with the highest health score may be more likely to win). Such information can be used in game contexts, such as fantasy sports teams, or to set odds for sports betting. The health score could be used for club competitions (eg group health improvement competitions, advertising based on a person's health score, game, TV/internet, etc.).
[0169] Thus, in a broad aspect, a method according to the invention can be understood as collecting health related information, processing the information into a health score and publishing the health score is provided. A system for implementing the method may include a computer having a processor, memory, and code modules running on the processor for collecting, processing, and publishing information. Information regarding a plurality of related health parameters of a user is collected, specifically, both intrinsic values referring to the measurable medical parameters of at least one natural person, and extrinsic values referring to the activities of each such person, such as, the exercise performed, the type of work the person has and the amount of physical work associated with the work (eg, sedentary, desk work versus active, manual labor intensive work) and/or calories/food consumed . Weighing factors are applied to the related health parameter to control the relative effect each parameter has on the user's calculated health score. The health score is computed using the processor by combining the weighted parameters in accordance with an algorithm. The health score is published to a designated group via a portal. In a deployment, the portal is a forum for the purpose of sharing information online.
[0170] As such, the invention can be characterized by the following points in a method for collecting and presenting health related data: collecting information regarding a plurality of health related parameters of a user; store the collected information in a memory; store weighing factors in memory; process the information collected by executing code on a processor that configures the processor to apply weighing factors to related health parameters; calculate a health score using the processor by combining the weighted parameters in accordance with an algorithm; and provide the health score to a designated group via a portal.
[0171] The methods described here have been described with respect to flowcharts that facilitate a description of the main processes; however, certain blocks can be invoked in an arbitrary order, such as when events trigger the program flow, such as in object-oriented program implementation. Correspondingly, flowcharts should be understood as exemplary flows, so that blocks can be invoked in a different order than that illustrated.
[0172] While the invention has been described with respect to certain embodiments thereof, the invention is not limited to the described embodiments, but is rather more broadly defined by the recitations in any of the following claims and their equivalents.
[0173] The object described above is provided by way of illustration only and should not be construed as limiting. Various modifications and changes can be made to the object described herein without following the exemplary embodiments and applications illustrated and described, and without deviating from the true spirit and scope of the present invention, which is set forth in the following claims.

权利要求:
Claims (12)
[0001]
1. Method for managing health-related data, characterized by the fact that it comprises the steps of: - receiving data in a memory representing data from a plurality of extrinsic parameters of a user's physical activity, where the step of receiving the extrinsic parameters of physical activity comprises: - capturing (300) one or more images from a display that is coupled to an exercise machine (107); - process the data included in the captured images; - identify the extrinsic parameters of physical activity from the processed data (320); - storing the received data in memory (330); - update a user-specific profile with previously received data captured in memory (330); and - provide the profile to the user through a portal (350).
[0002]
2. Method according to claim 1, characterized in that the step of extracting text is performed using an optical character recognition algorithm on the captured images.
[0003]
3. Method according to claim 1, characterized in that the processing comprises the application of an image comparison algorithm that produces a usable result in the identification step.
[0004]
4. Method according to claim 1, characterized in that the step of identifying the extrinsic parameters of physical activity further comprises: - identifying the strings of characters in the text; - categorize strings according to one or more data types; and - establish spatial relationships between sequences.
[0005]
5. Method according to claim 1, characterized in that it further comprises the step of identifying within the captured images a brand of the exercise machine manufacturer (107).
[0006]
6. Method according to claim 5, characterized in that it further comprises the step of extracting text from a limited area of the image as dictated by the brand of the exercise machine (107).
[0007]
7. Method according to claim 1, characterized in that it further comprises the steps of: - receiving (1230) in memory (330) a plurality of intrinsic medical parameters about the user; - store (1240) weighing factors in memory (330); - processing (1250) the received data by executing code in a processor that configures the processor to apply the weighing factors that are stored in memory (330) to intrinsic medical parameters and extrinsic physical activity parameters; - transforming (1260) the processed data (320) relating to the intrinsic medical parameters and extrinsic physical activity parameters by executing code on the processor into a masked composite numerical value in which the code is operative to combine the weighted parameters in accordance with an algorithm; and - automatically publish the masked composite numeric value to a designated group via the portal (350), using code running on the processor and free from human intervention, while maintaining the collected information regarding intrinsic medical parameters and extrinsic parameters of private physical activity.
[0008]
8. Method according to claim 7, characterized in that the weighing factors for the extrinsic physical activity parameters include a decay component arranged to reduce the relative weight of the extrinsic physical activity parameters for a physical activity in dependence on at least one factor associated with the user.
[0009]
9. Method according to claim 8, characterized in that the factor associated with the user is an age or a range of age of the user so that the decay component reduces the relative weight of the extrinsic parameters of physical activity to a first user of a first age or age range differently than a second user of a second age or age range.
[0010]
10. Method, according to claim 7, characterized in that the steps of processing, transforming and publishing are performed automatically by receiving data on the intrinsic or extrinsic medical parameters of a user.
[0011]
11. Method according to claim 1, characterized in that it comprises the additional steps of: - communicating at least a portion of the record to the exercise machine (107) and automatically establishing an exercise program on the exercise machine (107) with that base.
[0012]
12. Method according to claim 7, characterized in that the step of processing the received extrinsic physical activity parameters includes: - obtain a measure of the calories spent in physical activity in memory (330); - run additional code in the processor that configures the processor to: - transform measured calories into a metabolic equivalent value, MET, by dividing by the user's body weight; - split the MET value between a health pool and a bonus pool, with the bonus pool having a pre-determined size and any split MET value exceeding the bonus pool size is allocated to the health pool; and - apply a daily decay component to the bonus pool; the step of transforming the processed data (320) comprises combining the weighted medical parameters and a weighted health cluster value in accordance with the algorithm.

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同族专利:

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公开号 | 公开日

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AU2012257754A1|2013-11-21|

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US20120296455A1|2012-11-22|

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US20160378921A1|2016-12-29|

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CA2836381C|2021-03-09|

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RU2013148404A|2015-06-27|

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RU2607953C2|2017-01-11|

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EP2710503B1|2015-12-30|

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US10546103B2|2020-01-28|

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HK1195961A1|2014-11-28|

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CA2836381A1|2012-11-22|

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WO2012156374A1|2012-11-22|

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DK2710503T3|2016-03-07|

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BR112013029467A2|2020-08-11|

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EP2710503A1|2014-03-26|

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US20200227155A1|2020-07-16|

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US9378336B2|2016-06-28|

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法律状态:
2020-08-25| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|

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2020-09-01| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|

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2021-02-09| B06A| Notification to applicant to reply to the report for non-patentability or inadequacy of the application [chapter 6.1 patent gazette]|

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2021-05-18| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|

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2021-06-15| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 14/05/2012, OBSERVADAS AS CONDICOES LEGAIS. |

优先权:

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申请号 | 申请日 | 专利标题

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US201161486658P| true| 2011-05-16|2011-05-16|

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US61/486,658|2011-05-16|

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US13/423,051|2012-03-16|

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US13/423,051|US9378336B2|2011-05-16|2012-03-16|Optical data capture of exercise data in furtherance of a health score computation|

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PCT/EP2012/058946|WO2012156374A1|2011-05-16|2012-05-14|Optical data capture of exercise data in furtherance of a health score computation|

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