WO2016009229A1 - Wellness system - Google Patents

Wellness system Download PDF

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Publication number
WO2016009229A1
WO2016009229A1 PCT/GB2015/052095 GB2015052095W WO2016009229A1 WO 2016009229 A1 WO2016009229 A1 WO 2016009229A1 GB 2015052095 W GB2015052095 W GB 2015052095W WO 2016009229 A1 WO2016009229 A1 WO 2016009229A1
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WO
WIPO (PCT)
Prior art keywords
wellness
user
information
monitoring
performance information
Prior art date
Application number
PCT/GB2015/052095
Other languages
French (fr)
Inventor
Sergio Michelangelo Mottola
Original Assignee
Nuwe Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nuwe Limited filed Critical Nuwe Limited
Publication of WO2016009229A1 publication Critical patent/WO2016009229A1/en

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices

Definitions

  • the present invention relates to a system and method for monitoring and measuring wellness.
  • calorie consumption can be monitored based on the calorie content of meals consumed, and therefore a calorie controlled diet can attempt to ensure a maximum (or minimum) number of calories are consumed in a given period.
  • a point of sale system that calculates the nutritional value of a dish is described in WO 2010/082074.
  • the present invention aims to at least partly solve these problems
  • a system for monitoring and measuring wellness comprising: a memory configured to store targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; an input unit for providing performance information about the user relating to at least a first one of the wellness metrics; a calculation unit configured to calculate an updated progress against the first wellness metric based on the provided performance information, and a feedback unit configured to update the target for a wellness metric, based on the provided performance information relating to the first wellness metric.
  • This system can monitor a users progress against wellness targets, such as a daily nutritional intake or amount of exercise, and update the user's targets based on their performance. As such, by feeding back information into the targets, the user is provided with more personalised goals and is kept engaged and motivated to improve their lifestyle and keep to their fitness regime.
  • wellness targets such as a daily nutritional intake or amount of exercise
  • the feedback unit can be configured to update the target for a second wellness metric, based on the provided performance information relating to the first wellness metric.
  • a nutrition target could be updated based on the exercise a user performs.
  • one target may depend on multiple factors and can be recalculated based on incoming performance information relating to any or all of those factors.
  • the system can provide the most appropriate targets for the user, in a way that they would not be able to monitor and calculate for themselves.
  • the system can further comprise a sensor for collecting the performance information, or precursor data to the performance information, about at least the first metric.
  • the sensor can comprise a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
  • performance information can be automatically recorded, reducing the need for user interaction and thereby increasing the reliability/accuracy of the information, since users are prone to forget to input information themselves.
  • the system can comprise an output unit for outputting the progress against the targets for the wellness metrics.
  • the output unit can be a display or a speaker. The user is, therefore, easily able to monitor their progress towards their targets.
  • the memory can be further configured to store user schedule information and an interaction determining unit can be configured to provide an input prompt to the user, via the output unit and based on the schedule information, requesting input relating to metric-specific performance data.
  • the prompts also assist in the collection of more accurate/complete data, by reminding a user to input information that they would otherwise have forgotten to enter into the system. This ensures that the user's progress towards their targets is recorded as accurately as possible, and thus allows the setting of a user's targets to be made based on the most complete information possible.
  • the interaction determining unit can be further configured to provide the input prompt based on time and/or location of the user. As such, the prompts can relate to activities a user normally performs at certain times or locations, ensuring they are as relevant as possible.
  • the feedback unit can be further configured to update the schedule information based upon a history of the performance information.
  • the feedback unit can be further configured to update the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations. In this way, the system 'learns' a user's habits, and so more relevant prompts can be provided, customising the system to the user's lifestyle.
  • the system can further comprises a clock and/or user location determining unit; and the interaction determining unit can be further configured to provide a suggestion, via the output unit and based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
  • the memory can be further configured to store location information, the location information including a place and information related to at least one of the wellness metrics; and the interaction determining unit, when the user location determining unit determines that a user is within a predetermined distance of a place recorded in the location information, is further configured to provide a suggestion based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target.
  • Such suggestions could be, for example, to take a particular type/amount of exercise because a particular sports facility is nearby.
  • the system can assist the user in making decisions that contribute towards achieving the user's targets.
  • the memory can be further configured to store conversion information, for converting performance information into progress against one or more targets; and the calculation unit can be configured to calculate the updated progress from the performance information with reference to the conversion information.
  • the system can process incoming performance information to extract the necessary information to determine how it relates to the user's targets.
  • a method of monitoring and measuring wellness comprising: storing targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; receiving performance information about the user relating to a first one of the wellness metrics; calculating an updated progress against the first wellness metric based on the provided performance information, and updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric.
  • Updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric can comprise updating the target for a second wellness metric.
  • the method can further comprise: collecting, with a sensor, the performance information, or precursor data to the performance information, about at least the first metric.
  • the sensor can comprise a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
  • the method can further comprise outputting the progress against the targets for the wellness metrics.
  • the outputting can be performed via a display or a speaker.
  • the method can further comprise storing user schedule information; and providing an input prompt to the user, based on the schedule information, requesting input relating to metric-specific performance data.
  • the input prompt can be based on time and/or location of the user.
  • the method can further comprise updating the schedule information based upon a history of the performance information.
  • the method can further comprise updating the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations.
  • the method can further comprise providing a suggestion, based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
  • the method can further comprise storing location information, the location information including a place and information related to at least one of the wellness metrics; and providing a suggestion, when it is determined that a user is within a predetermined distance of a place recorded in the location information, based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target.
  • the method can further comprise storing conversion information, for converting
  • a storage medium storing computer readable code for implementation by a computer or network of computers, the code, when implemented, causing the computer or network of computers to implement the steps of the method of the preceding aspect.
  • a computerised system for monitoring and measuring wellness comprising: a memory configured to store targets for the user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; an input device for providing performance information about the user relating to a first one of the wellness metrics; a processor configured to calculate an updated progress against the first wellness metric based on the provided performance information, and wherein the processor is further configured to update the target for a second metric, based on the provided performance information relating to the first wellness metric.
  • Fig. 1 is diagram of a wellness monitoring and measuring system
  • Fig. 2 is a schematic flowchart describing aspects of the operation of a wellness system.
  • the present invention provides a method and system for monitoring and measuring wellness. Further, the system can provide advice to assist a user with making healthy choices throughout the course of their day. This is achieved by taking input from various sources and sensors, so that the system is able to monitor the user's progress towards particular health-related targets and also learn the user's habits to assist with making appropriate suggestions for attaining those targets in a way that fits with the user's lifestyle.
  • FIG. 1 shows an example system 100.
  • the primary components of the system are a server 10 and a client device 20, which communicate with each other via a network 30.
  • the network 30 could be a local area network (LAN), wide area network (WAN), or any other network such as the internet or telephone network, for example.
  • LAN local area network
  • WAN wide area network
  • any other network such as the internet or telephone network, for example.
  • the system 100 shown in Fig. 1 presents a separate server 10 and client device 20, this separation is not essential to the invention.
  • Alternative embodiments could combine the features of the server 10 in the client device 20.
  • a user may have several client devices 20 that communicate with the server 10 (and the server 10 itself could in fact be a plurality of separate server units).
  • the server 10 has a memory 11, which serves to store various types of information in a database. That information is discussed in more detail below, but relates to various health-related metrics. This information can include "static" user information which is entered and not changed (or only changed irregularly) such as a user's name and/or date of birth for example. It can also include "performance information", which is information relevant to assessing the user's activities against the various health-related metrics.
  • the user-specific information stored in the database also includes “targets” for a user in connection with the wellness metrics, and user “schedule information”.
  • the database may also contain "location information", the location information including both the actual (geographical) location (e.g.
  • the database may also include "conversion information", used to convert incoming performance information into a measure of progress against the targets. All these types of information are now discussed in more detail below.
  • the health-related/wellness metrics considered in the system can relate to any aspect of a user's health. Examples could include “nutrition”, “exercise” and “biometrics”. However, this is not a limiting list, and other metrics could also be included.
  • Such metrics can be an amalgam of various factors.
  • a "nutrition” metric may relate to calorie consumption as well as consumption of particular amounts of food constituents (e.g. recommended daily allowances of vitamins and minerals).
  • an “exercise metric” may relate to the number of calories burnt during exercise, as well as the different types of exercise (e.g. cardiovascular, upper body strength, lower body strength, stretching) performed.
  • a glucose metric just discussed, it may be desirable to provide a separate metric specifically for calories if a user is particularly concerned about their calorie intake.
  • a general "biometrics" metric may incorporate information about a user's weight, their heart rate, their breathing rate, as well as information from medical tests (e.g. blood sugar levels).
  • a diabetic user may have particular interest in blood sugar level, and therefore desire to see a separate metric for blood sugar levels as well as an overall biometrics metric.
  • a target for daily calorie intake can be calculated based upon the recommended daily calorie intake for a person, in view of their age, gender and BMI.
  • Targets for metrics monitoring an amalgam of parameters, such as the "nutrition" metric discusses above, can be set based on weighted contributions from each of the parameters of interest. For example, 60% of the "nutrition" target might relate to calorie intake, whilst 40% might relate to vitamin/mineral intake.
  • the target for one metric can be dictated by factors relating to other metrics. For example, a user's weight will change over time, and may be monitored via a "biometrics" metric. The changing weight will cause a change in BMI. Therefore, the change in weight will be relevant to setting the correct "nutrition" target.
  • Another type of metric can be considered a 'goal based' metric. For example, a user may wish to complete a marathon in six month's time, or lose a certain amount of weight over a particular period, or lower their cholesterol levels. A particular metric for each of these 'goals' can be constructed, and tailored to the user's goal based on their current information (e.g.
  • the user specific "performance information" relates to a user's activities in connection with the various wellness metrics.
  • the performance information in connection with the nutrition metric can include information about the food a user has eaten (including the type and amount thereof), which can then be used to determine the progress of the user towards a target (i.e. the total amount of calories to be eaten within a day, and the consumption of the recommended daily allowance of necessary vitamins and minerals).
  • a target i.e. the total amount of calories to be eaten within a day, and the consumption of the recommended daily allowance of necessary vitamins and minerals.
  • the performance information could relate to the amount of energy expended during exercise, and/or the completion of specified types of exercise (e.g. if a user is in training for a particular purpose).
  • the performance information needs to be processed based on the stored "conversion information" to determine the associated progress. For example, whilst performance information relating to a number of calories consumed might explicitly represent progress against a "calorie” metric, some further processing (according to a predetermined rule) will be needed to assess the progress against a general "nutrition” metric that relates to more parameters than solely calorie intake. Similarly, performance information in the form of a meal report (e.g. "hamburger and chips, slice of cake, small soda”) would need converting into calories, based on stored conversion information regarding the calorie content of certain foods, to determine progress against even a "calorie” metric.
  • a meal report e.g. "hamburger and chips, slice of cake, small soda
  • the performance information may be archived to generate a "history" of performance information which can be analysed to understand a user's habits. For example, it may be derived from the history of performance information that a user runs for an hour every morning, or has a particular meal at a particular time and location.
  • the "location information" stored in the memory 11 combines information about the geographical location of a place with other information, related to that location and the wellness metrics. For example, location information about a particular cafe might include nutrition information about particular meals, whilst information about a leisure complex might include the availability of particular training equipment/environments (such as the presence of a swimming pool).
  • the user specific "schedule information” includes details of a user's routine. This routine can encompass both a user's "health” regime as well as a more general "life routine". For example, the schedule information might include information about a user's normal working hours, as well as information that the user normally cycles to work. It can also include information about specific appointments the user has to keep.
  • the schedule information can be, in the first place, derived from (and periodically, or aperiodically, updated from) an electronic calendar for example.
  • the schedule information can be supplemented by the "habit information" mentioned above. That is, if the system identifies, based on the performance information received, that a user performs a certain type of exercise at a certain time of a specific day, every week, that information can be incorporated into the schedule information (even if it is not originally present in the calendar data, for example). As such, the system is capable of learning the user's routines and is therefore better able to interact with the user (as discussed in more detail later).
  • the server 10 further comprises a calculation unit 12, which could be embodied by a computer processor for example.
  • the calculation unit 12 performs the necessary calculations with the data saved in the memory 11, in order to determine the target for each of the metrics, and progress towards each target (for example, by processing the performance information with the conversion information). For example, the calculation unit may set the target for a "nutrition" metric based on the age, gender and how much exercise a user performs, and according to standard recommendations and guidelines known in the field (i.e. recommended daily consumption amounts).
  • the calculation unit will also process incoming performance information, to calculate a user's progress against the target. For example, details of the length and type of an exercise performed might be provided to the calculation unit as performance information in connection with an "exercise" metric. The calculation unit would then process that performance information using the relevant conversion information, which in this example could comprise average calorie burn rates for different types of exercise, to transform the performance information into a number of calories burned during the exercise. This in turn may need to be converted into progress against the "exercise" target, if that metric is involves more parameters than just the number of calories burnt.
  • the server 10 also comprises a feedback unit 13, which may also be embodied by a computer processor. The feedback unit 13 serves a first function of updating the user targets based upon incoming performance data for other metrics. In some cases the feeback unit 13 could even update the target for a metric based on incoming performance information for the same metric.
  • the "push-up" target for the next day might be reduced to make the training programme more appropriate to the user.
  • the feedback unit 13 monitors the progress against the various metric targets and the inter-relationships between the metrics. These inter-relationships may be known, but it is currently infeasible for a user to track the consequences of these inter-relationships with any accuracy, and so they cannot tailor their lifestyle or exercise regime appropriately.
  • the system 100 provided with the relevant performance information, can transform the performance information into both progress against the user's targets and also adjustments to those targets, as appropriate.
  • the feedback unit 13 can continuously or periodically (or aperiodically) update the metric targets based on the progress information received in connection with other metrics. For example, the feedback unit 13 may update the targets every day, based on the previous day's performance data. The frequency of update may vary from one metric to another. For example, it may be more appropriate to update "exercise" targets more regularly than "nutrition" targets.
  • Each metric target may be updated in response to progress against one or more other metrics.
  • the feedback unit 13 may update that target in response to exercise performance information collected in connection with an "exercise” metric, and also in connection with weight measurements collected in connection with a “biometrics” metric.
  • the feedback unit 13 serves a second function of updating the schedule
  • the particular conditions required to update the schedule information can vary, but could be related, within a margin of error, to the frequency (e.g. daily, weekly or monthly) of a particular activity or type of "performance information" being logged over a predetermined time. For example if performance data indicating that a user cycles in the morning is recorded for more than 8 days out of 10 in a row, a schedule update might be made to include a daily cycle event. Alternatively, if the schedule indicates a user goes swimming every Saturday afternoon, but corresponding performance information is not recorded two weeks out of three in a row, the schedule item could be deleted.
  • the server 10 also comprises an interaction determining unit 14, which can also be embodied by a computer processor for example.
  • the interaction determining unit 14 can serve one or both of two functions: firstly, it can be configured to send prompts to the user to record that particular actions have been performed and, secondly, it can provide suggestions of activities that the user could perform.
  • the server 10 also comprises a clock 15.
  • the provision of prompts encourages a user to provide information that is necessary for the system to work effectively (i.e. by making the information providing as complete as possible).
  • the interaction determining unit 14 might send a prompt in the morning, asking the user to confirm that they have had their normal breakfast, to contribute performance information for a
  • the interaction determining unit 14 might send a prompt asking a user how they got to work, the response to which could in turn generate performance data regarding the "exercise metric" if a user walked or cycled to work.
  • the suggestion function of the interaction determining unit 14 is intended to assist the user in achieving their targets. As such, a suggestion could be given to visit a particular cafe and have a particular meal or drink in order to progress against a "nutrition" target. In some cases, the suggestion could provide the user with options (e.g. the suggestion might mention three menu items for the user to select from). Alternatively, if a user was passing a gym, the interaction determining unit 14 might provide a suggestion to go to the gym and perform a particular set of exercises that would be most appropriate for progressing towards their "exercise" target. The suggestions can take into account the user's schedule. For example, a suggestion to visit a nearby gym might not be issued if the user's schedule has a gym session later in the day.
  • the server 10 communicates with a client device 20.
  • the user interfaces with the client device 20 via an input unit or device 21.
  • the input unit 21 could be a keyboard (either integrated with the client device or connected via a cable or wireless connection) or touch-screen if the client device is a smartphone or portable device, for example.
  • the client device 20 is a personal computer, the input unit could incorporate a keyboard and/or a mouse, and/or a microphone provided with voice recognition.
  • the client device 20 is also provided with an output unit 22.
  • An output unit can be any means of providing information to the user. As such, if the client device 20 is a smartphone or other portable tablet or computer, the output unit may be a screen and/or a microphone and/or a force feedback/vibration creating device. As such, prompts and suggestions issued by the interaction determining unit 14 could be presented as textual messages on an output 22 display, or could be presented as spoken words via a speaker.
  • the client device 20 can optionally also include one or more sensors 23.
  • the client device 20 may be able to directly monitor some parameters relevant to one or more of the wellness metrics, without direct input from the user, or with reduced input.
  • a sensor 23 could be a GPS device, which can be used to monitor a user's movement, which in turn could generate performance information in connection with an "exercise" metric. For example, this could be entirely automatic, based on algorithm to determine a user's speed and therefore likely mode of transportation. Alternatively, a user could confirm that certain journeys recorded via the GPS throughout the day were performed in a certain manner (i.e. through walking or cycling).
  • the sensor 23 could incorporate an accelerometer, configured to measure the number of steps taken by a user, which could also be sent to the server 20 as "exercise" performance data.
  • a sensor may be present as a separate sensor device 40 (which could include a sensor unit embedded in a larger device).
  • the sensor device 40 could provide information direct to the client device 20 or server 10 regarding the parameters it is measuring, as appropriate.
  • a fully automated sensor that can provide performance information without user input might communicate directly with the server 20.
  • a sensor requiring additional user input before performance information can be generated might communicate with the client device 20.
  • the server 10, client device 20, and sensor device 40 can communicate through respective communication unitsl9, 29, 49, via the network 30.
  • a sleep sensor might determine that the user has had eight hours deep sleep and two hours light sleep, for example. This data could be relevant as performance information to an "exercise” metric (which might, for example, incorporate a measure of rest with a measure of activity performed, when creating an overall "exercise” target), or it might contribute to a "biometrics" target. In any case, the performance information is sent to the calculation unit 12, which would calculate the necessary progress against the appropriate targets stored in memory 11.
  • the interaction determining unit 14 might then recognise that it is time for breakfast, and suggest a suitable breakfast based on for example, the amount of exercise recorded in the previous day and/or the user's "nutrition" target.
  • the suggestion could be at any level of specificity. For example, it might suggest having a "protein rich” breakfast, or it might suggest having a particular breakfast meal that the user regularly has (and so presumably likes), or might suggest different options (e.g. the three best breakfast options based on the user's targets and performance history).
  • the user might then proceed to eat breakfast. They could either proceed to input the information about the breakfast consumed, via a client device 20 which could be a home laptop.
  • the interaction determining unit 14 may send a prompt if it determines that the user's normal breakfast time has passed without any breakfast information being input.
  • the prompt might be picked up by the user on a mobile phone, acting as another client device.
  • the information about the breakfast could be input by selecting a known meal/recipe, which would then be converted in the calculation unit 12 (using the relevant conversion information) into progress information in connection with a "nutrition" metric.
  • the interaction determining unit 14 might suggest a method of commuting. Once again, this could be based upon the user's recent exercise history. Therefore, it could be suggested that a user take a bus if they exercised excessively the day before, or cycle if the user needs to do more exercise and is sufficiently rested.
  • the user might then cycle to work.
  • the client device could collect data regarding the distance travelled and the time taken, for example, to derive an average speed and a level of exercise intensity. It could be that this information is recorded in a specific cycling app on a smartphone, for example. In that case there would be no need for the user to provide any additional information about the type of exercise, before the performance information is sent to the calculation unit.
  • a sensor such as a GPS sensor might monitor the route of a user, and the user might subsequently confirm that the route was cycled.
  • the receipt of the cycling performance information at the server 20 might also trigger the feedback unit 13 to update the user's schedule (e.g. because the user has cycled to work every day for the past week) to include cycling to work in the user's schedule.
  • the result of this update could be that, the next morning, the system might not suggest taking the bus (because it expects the user to cycle) and might suggest an alternative breakfast.
  • the interaction determining unit 14 might suggest taking a break from intensive exercise. If the user performs a sedentary job, step and/or accelerometer and/or location sensors might determine that a user does not appreciably move for four hours between arriving at work and lunchtime. As for sleeping, such data might positively contribute towards a "exercise metric" that balances sufficient rest against sufficient activity.
  • the interaction determining unit 14 in response to the time, might suggest a specific lunch for the user. For example, based on the user having performed intensive activity in the form of cycling to work, the interaction determining unit 14 might suggest having a high calorie/high protein meal for lunch. If the user is close to a place having location information stored in memory 11 , the suggestion provided by the interaction determining unit may even specify a particular meal (i.e. a beef sandwich) from a particular nearby shop as being the best meal option locally available. Of course, if the user needs to walk to obtain their lunch, this exercise can be also captured and turned into performance information.
  • a particular meal i.e. a beef sandwich
  • the user After eating lunch, the user will input what they consumed through their input unit 21, which will be sent to the server 10 as performance information and processed in the same way as the breakfast information.
  • the interaction determining unit 14 may also suggest a light exercise such as walking after lunch, to aid digestion. Once again, this could generate exercise performance information.
  • the interaction determining unit 14 might determine that, based on GPS information from a sensor 23 or 40 and the schedule information in the memory 11, the user is heading towards the gym. As such, it might issue a suggestion that the user has a light snack before they start training. If the user acts on the suggestion, and inputs the relevant information, that information will be turned into performance information recorded against the "nutrition" target.
  • the interaction determining unit 14 might determine that a user has done enough cardiovascular exercise that day (e.g. through cycling to work) and therefore suggests that the user focuses on muscle-building activities whilst at the gym.
  • the user might choose to ignore the suggestion to focus on muscle-building, and instead spend time at the gym running. This could generate performance information which reduces the users progress towards their "exercise target", if they have already done enough cardiovascular exercise that day. Such "negative” performance information could also cause the interaction determining unit to issue a suggestion to eat additional carbohydrates in order to compensate for the over-exercise, for example.
  • the user might go home for dinner, but forget to enter the details of their dinner into the system 100. Therefore, later that night, the interaction determining unit 14 might issue a prompt asking the using to input the relevant information about their evening meal.
  • the interaction determining unit 14 might issue a suggestion that the user performs some light exercise such as stretching or yoga to avoid stiffness after the exercise- intensive day. Performing this activity could generate performance information relating to the parameters of the number of calories burned and also the type of activity performed. For example, the exercise metric might measure whether yoga is performed every day, and allocate progress towards the "exercise" target based on the fact that some (i.e. any) yoga has been performed. That is, one aspect of the "exercise” metric could be a "check list" of different types of exercise that should be performed throughout the day.
  • the interaction determining unit 14 may also suggest that the user weighs themselves before going to bed.
  • the system 100 could interact directly with "smart scales" acting as a sensor device 40, or the user could input their weight information through the input unit 21. Irrespective of the way the information is recorded, it could contribute performance information towards a user's "biometrics" target, for example. That is, the biometrics "metric” might also operate as a "check list" for activities such as a user weighing themselves or taking their blood pressure and heart rate, or performing activities such as taking blood tests (e.g. if a user has diabetes for example).
  • the system 100 can provide various types of prompts and suggestions based upon the user's activity history. Importantly, however, the system is also able to "learn" from the user's habits. [0087] As such, the system can adapt to the requirements of the user and provide a more personalised experience. This in turn makes it more likely that the user will remain motivated, as the prompts and suggestions will be appropriate to them, and so less likely to become an annoyance which the user will wish to simply ignore rather than respond to.
  • Fig. 2 shows the operation of aspects of the wellness system in terms of a steps in a flowchart.
  • step S201 the system 100 is initiated.
  • a user might input basic or "static" information about themselves (or non-static information that will subsequently be updated by the system) and select the metrics they wish to use.
  • step S202 the targets for the various metrics are calculated and stored.
  • schedule information is obtained and stored (for example from an electronic calendar) if it is available.
  • step S203 performance information is received.
  • the performance information could be received directly/automatically from a sensor 23,40 or could be the result of user input through an input device 21.
  • the performance information is processed in steps S204 and S205, which may occur concurrently.
  • the performance information is used, for example by the calculation unit 12 to calculate progress against the targets. This may involve use of conversion information, as discussed previously.
  • the progress may then be sent for output at step S206.
  • step S205 the performance information obtained in step S203 is used to update the user's schedule information and/or their targets.
  • schedule information could be updated to include that exercise every day (if this is determined to be appropriate based on the performance information history) and the target for a "nutrition" metric could be updated to increase the number of calories a user should consume.
  • This updating can be done in response to the incoming 'raw' performance information or the 'processed' progress against the metrics, as appropriate (e.g. it may be appropriate in the preceding example to update the schedule based on the raw information about the type of exercise, whilst the "nutrition" metric might be updated on the processed progress against the relevant target) .

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Abstract

A system for monitoring and measuring wellness comprises a memory configured to store targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; an input unit for providing performance information about the user relating to at least a first one of the wellness metrics; a calculation unit configured to calculate an updated progress against the first wellness metric based on the provided performance information, and a feedback unit configured to update the target for a wellness metric, based on the provided performance information relating to the first wellness metric.

Description

WELLNESS SYSTEM
[0001] FIELD OF THE INVENTION
[0002] The present invention relates to a system and method for monitoring and measuring wellness.
[0003] BACKGROUND TO THE INVENTION
[0004] There are various parameters or metrics that can be used to monitor an individual's health or wellness. For example, these include calorie intake, the amount of exercise performed, and BMI (Body Mass Index) amongst others. They can also include specific medical measures (such as blood pressure or blood sugar levels) that might be monitored in connection with a particular medical issue (such as high blood pressure or diabetes).
[0005] The are various methods of monitoring these metrics individually, and even for setting targets and measuring performance against those targets. For example, calorie consumption can be monitored based on the calorie content of meals consumed, and therefore a calorie controlled diet can attempt to ensure a maximum (or minimum) number of calories are consumed in a given period. There are even technical systems which aim to assist in this process, for example a point of sale system that calculates the nutritional value of a dish is described in WO 2010/082074.
[0006] However, such prior methods and systems suffer from the problem that an individual's wellness is the result of an amalgam of different metrics. Those metrics interact with each other, such that targets set for one metric can become inaccurate as a result of over- or under-performance of an individual against a target for a different metric.
[0007] Further, such methods and systems rely upon an individual recording their activities and actively seeking out, for example, opportunities to exercise or appropriate food to consume. Even though such information is increasingly readily available, over the internet for example, such planning and recording can be time consuming. As a result, individuals can lose motivation and/or miss opportunities to keep to their wellness regime.
[0008] The present invention aims to at least partly solve these problems
[0009] SUMMARY OF INVENTION
[0010] According to an aspect of the present invention there is provided a system for monitoring and measuring wellness, the system comprising: a memory configured to store targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; an input unit for providing performance information about the user relating to at least a first one of the wellness metrics; a calculation unit configured to calculate an updated progress against the first wellness metric based on the provided performance information, and a feedback unit configured to update the target for a wellness metric, based on the provided performance information relating to the first wellness metric.
[0011] This system can monitor a users progress against wellness targets, such as a daily nutritional intake or amount of exercise, and update the user's targets based on their performance. As such, by feeding back information into the targets, the user is provided with more personalised goals and is kept engaged and motivated to improve their lifestyle and keep to their fitness regime.
[0012] The feedback unit can be configured to update the target for a second wellness metric, based on the provided performance information relating to the first wellness metric. In this case, for example, a nutrition target could be updated based on the exercise a user performs. In fact, one target may depend on multiple factors and can be recalculated based on incoming performance information relating to any or all of those factors. As such, the system can provide the most appropriate targets for the user, in a way that they would not be able to monitor and calculate for themselves.
[0013] The system can further comprise a sensor for collecting the performance information, or precursor data to the performance information, about at least the first metric. The sensor can comprise a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
[0014] As such, performance information can be automatically recorded, reducing the need for user interaction and thereby increasing the reliability/accuracy of the information, since users are prone to forget to input information themselves.
[0015] The system can comprise an output unit for outputting the progress against the targets for the wellness metrics. The output unit can be a display or a speaker. The user is, therefore, easily able to monitor their progress towards their targets.
[0016] The memory can be further configured to store user schedule information and an interaction determining unit can be configured to provide an input prompt to the user, via the output unit and based on the schedule information, requesting input relating to metric-specific performance data. The prompts also assist in the collection of more accurate/complete data, by reminding a user to input information that they would otherwise have forgotten to enter into the system. This ensures that the user's progress towards their targets is recorded as accurately as possible, and thus allows the setting of a user's targets to be made based on the most complete information possible. [0017] The interaction determining unit can be further configured to provide the input prompt based on time and/or location of the user. As such, the prompts can relate to activities a user normally performs at certain times or locations, ensuring they are as relevant as possible.
[0018] The feedback unit can be further configured to update the schedule information based upon a history of the performance information. The feedback unit can be further configured to update the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations. In this way, the system 'learns' a user's habits, and so more relevant prompts can be provided, customising the system to the user's lifestyle.
[0019] The system can further comprises a clock and/or user location determining unit; and the interaction determining unit can be further configured to provide a suggestion, via the output unit and based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
[0020] The memory can be further configured to store location information, the location information including a place and information related to at least one of the wellness metrics; and the interaction determining unit, when the user location determining unit determines that a user is within a predetermined distance of a place recorded in the location information, is further configured to provide a suggestion based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target. Such suggestions could be, for example, to take a particular type/amount of exercise because a particular sports facility is nearby. As such, the system can assist the user in making decisions that contribute towards achieving the user's targets.
[0021] The memory can be further configured to store conversion information, for converting performance information into progress against one or more targets; and the calculation unit can be configured to calculate the updated progress from the performance information with reference to the conversion information. As such, the system can process incoming performance information to extract the necessary information to determine how it relates to the user's targets.
[0022] According to another aspect of the invention, there is provided a method of monitoring and measuring wellness, the method comprising: storing targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; receiving performance information about the user relating to a first one of the wellness metrics; calculating an updated progress against the first wellness metric based on the provided performance information, and updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric. [0023] Updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric, can comprise updating the target for a second wellness metric.
[0024] The method can further comprise: collecting, with a sensor, the performance information, or precursor data to the performance information, about at least the first metric. The sensor can comprise a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
[0025] The method can further comprise outputting the progress against the targets for the wellness metrics. The outputting can be performed via a display or a speaker.
[0026] The method can further comprise storing user schedule information; and providing an input prompt to the user, based on the schedule information, requesting input relating to metric-specific performance data. The input prompt can be based on time and/or location of the user.
[0027] The method can further comprise updating the schedule information based upon a history of the performance information.
[0028] The method can further comprise updating the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations.
[0029] The method can further comprise providing a suggestion, based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
[0030] The method can further comprise storing location information, the location information including a place and information related to at least one of the wellness metrics; and providing a suggestion, when it is determined that a user is within a predetermined distance of a place recorded in the location information, based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target.
[0031] The method can further comprise storing conversion information, for converting
performance information into progress against one or more targets; and calculating the updated progress from the performance information with reference to the conversion information.
[0032] According to another aspect of the invention, there is provided a storage medium storing computer readable code for implementation by a computer or network of computers, the code, when implemented, causing the computer or network of computers to implement the steps of the method of the preceding aspect.
[0033] According to another aspect of the invention, there is provided a computerised system for monitoring and measuring wellness, the system comprising: a memory configured to store targets for the user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics; an input device for providing performance information about the user relating to a first one of the wellness metrics; a processor configured to calculate an updated progress against the first wellness metric based on the provided performance information, and wherein the processor is further configured to update the target for a second metric, based on the provided performance information relating to the first wellness metric.
[0034] FIGURES
[0035] The present invention is described below, by way of example only, with reference to the accompanying Figures, in which:
Fig. 1 is diagram of a wellness monitoring and measuring system; and
Fig. 2 is a schematic flowchart describing aspects of the operation of a wellness system.
[0036] DESCRIPTION OF THE INVENTION
[0037] The present invention provides a method and system for monitoring and measuring wellness. Further, the system can provide advice to assist a user with making healthy choices throughout the course of their day. This is achieved by taking input from various sources and sensors, so that the system is able to monitor the user's progress towards particular health-related targets and also learn the user's habits to assist with making appropriate suggestions for attaining those targets in a way that fits with the user's lifestyle.
[0038] Figure 1 shows an example system 100. In this example, the primary components of the system are a server 10 and a client device 20, which communicate with each other via a network 30. The network 30 could be a local area network (LAN), wide area network (WAN), or any other network such as the internet or telephone network, for example. Further, although the system 100 shown in Fig. 1 presents a separate server 10 and client device 20, this separation is not essential to the invention. Alternative embodiments could combine the features of the server 10 in the client device 20. Alternatively, a user may have several client devices 20 that communicate with the server 10 (and the server 10 itself could in fact be a plurality of separate server units).
[0039] The server 10 has a memory 11, which serves to store various types of information in a database. That information is discussed in more detail below, but relates to various health-related metrics. This information can include "static" user information which is entered and not changed (or only changed irregularly) such as a user's name and/or date of birth for example. It can also include "performance information", which is information relevant to assessing the user's activities against the various health-related metrics. The user-specific information stored in the database also includes "targets" for a user in connection with the wellness metrics, and user "schedule information". The database may also contain "location information", the location information including both the actual (geographical) location (e.g. as a name and/or GPS coordinates) and also additional information related to the wellness metrics and associated with that particular place. The database may also include "conversion information", used to convert incoming performance information into a measure of progress against the targets. All these types of information are now discussed in more detail below.
[0040] The health-related/wellness metrics considered in the system can relate to any aspect of a user's health. Examples could include "nutrition", "exercise" and "biometrics". However, this is not a limiting list, and other metrics could also be included.
[0041] Such metrics can be an amalgam of various factors. For example, a "nutrition" metric may relate to calorie consumption as well as consumption of particular amounts of food constituents (e.g. recommended daily allowances of vitamins and minerals). As another example, an "exercise metric" may relate to the number of calories burnt during exercise, as well as the different types of exercise (e.g. cardiovascular, upper body strength, lower body strength, stretching) performed.
[0042] Nonetheless, it may also be desirable to have specific metrics for particular wellness related aspects that may also be included in a more general metric. For example, in addition to the
"nutrition" metric just discussed, it may be desirable to provide a separate metric specifically for calories if a user is particularly concerned about their calorie intake. As another example, a general "biometrics" metric may incorporate information about a user's weight, their heart rate, their breathing rate, as well as information from medical tests (e.g. blood sugar levels). However, a diabetic user may have particular interest in blood sugar level, and therefore desire to see a separate metric for blood sugar levels as well as an overall biometrics metric.
[0043] For each metric, there is an associated target based upon the available information about the user. For example, a target for daily calorie intake can be calculated based upon the recommended daily calorie intake for a person, in view of their age, gender and BMI. Targets for metrics monitoring an amalgam of parameters, such as the "nutrition" metric discusses above, can be set based on weighted contributions from each of the parameters of interest. For example, 60% of the "nutrition" target might relate to calorie intake, whilst 40% might relate to vitamin/mineral intake.
[0044] As will be discussed later, the target for one metric can be dictated by factors relating to other metrics. For example, a user's weight will change over time, and may be monitored via a "biometrics" metric. The changing weight will cause a change in BMI. Therefore, the change in weight will be relevant to setting the correct "nutrition" target. [0045] Another type of metric can be considered a 'goal based' metric. For example, a user may wish to complete a marathon in six month's time, or lose a certain amount of weight over a particular period, or lower their cholesterol levels. A particular metric for each of these 'goals' can be constructed, and tailored to the user's goal based on their current information (e.g. their current fitness levels, weight or cholesterol levels for each of the examples previously mentioned). As such, progress against these metrics could be measured, for example, based on completion of a particular training program each day (in the case of training for a marathon), or a user's food consumption (in the case of reducing cholesterol) or a combination of such factors (in the case of losing weight). Such 'goal based' metrics may only be relevant for a short period time (e.g. until the marathon has been run) compared to 'daily' metrics that will be ongoing.
[0046] The user specific "performance information" relates to a user's activities in connection with the various wellness metrics. For example, the performance information in connection with the nutrition metric can include information about the food a user has eaten (including the type and amount thereof), which can then be used to determine the progress of the user towards a target (i.e. the total amount of calories to be eaten within a day, and the consumption of the recommended daily allowance of necessary vitamins and minerals). For an exercise metric, the performance information could relate to the amount of energy expended during exercise, and/or the completion of specified types of exercise (e.g. if a user is in training for a particular purpose).
[0047] In order to make the determination of progress, it may be that the performance information needs to be processed based on the stored "conversion information" to determine the associated progress. For example, whilst performance information relating to a number of calories consumed might explicitly represent progress against a "calorie" metric, some further processing (according to a predetermined rule) will be needed to assess the progress against a general "nutrition" metric that relates to more parameters than solely calorie intake. Similarly, performance information in the form of a meal report (e.g. "hamburger and chips, slice of cake, small soda") would need converting into calories, based on stored conversion information regarding the calorie content of certain foods, to determine progress against even a "calorie" metric.
[0048] The performance information may be archived to generate a "history" of performance information which can be analysed to understand a user's habits. For example, it may be derived from the history of performance information that a user runs for an hour every morning, or has a particular meal at a particular time and location.
[0049] The "location information" stored in the memory 11 combines information about the geographical location of a place with other information, related to that location and the wellness metrics. For example, location information about a particular cafe might include nutrition information about particular meals, whilst information about a leisure complex might include the availability of particular training equipment/environments (such as the presence of a swimming pool).
[0050] The user specific "schedule information" includes details of a user's routine. This routine can encompass both a user's "health" regime as well as a more general "life routine". For example, the schedule information might include information about a user's normal working hours, as well as information that the user normally cycles to work. It can also include information about specific appointments the user has to keep.
[0051] The schedule information can be, in the first place, derived from (and periodically, or aperiodically, updated from) an electronic calendar for example. However, the schedule information can be supplemented by the "habit information" mentioned above. That is, if the system identifies, based on the performance information received, that a user performs a certain type of exercise at a certain time of a specific day, every week, that information can be incorporated into the schedule information (even if it is not originally present in the calendar data, for example). As such, the system is capable of learning the user's routines and is therefore better able to interact with the user (as discussed in more detail later).
[0052] The server 10 further comprises a calculation unit 12, which could be embodied by a computer processor for example. The calculation unit 12 performs the necessary calculations with the data saved in the memory 11, in order to determine the target for each of the metrics, and progress towards each target (for example, by processing the performance information with the conversion information). For example, the calculation unit may set the target for a "nutrition" metric based on the age, gender and how much exercise a user performs, and according to standard recommendations and guidelines known in the field (i.e. recommended daily consumption amounts).
[0053] The calculation unit will also process incoming performance information, to calculate a user's progress against the target. For example, details of the length and type of an exercise performed might be provided to the calculation unit as performance information in connection with an "exercise" metric. The calculation unit would then process that performance information using the relevant conversion information, which in this example could comprise average calorie burn rates for different types of exercise, to transform the performance information into a number of calories burned during the exercise. This in turn may need to be converted into progress against the "exercise" target, if that metric is involves more parameters than just the number of calories burnt. [0054] The server 10 also comprises a feedback unit 13, which may also be embodied by a computer processor. The feedback unit 13 serves a first function of updating the user targets based upon incoming performance data for other metrics. In some cases the feeback unit 13 could even update the target for a metric based on incoming performance information for the same metric.
[0055] For example, if a user is in a training programme which suggests doing an increasing number of push-ups every day, but the user records less than the target number of push-ups for that day, the "push-up" target for the next day might be reduced to make the training programme more appropriate to the user. Alternatively, as discussed above, if a user is performing a lot of exercise, it may be desirable to alter the "nutrition" target to set a higher target and encourage the user to consume more calories.
[0056] As such, the feedback unit 13 monitors the progress against the various metric targets and the inter-relationships between the metrics. These inter-relationships may be known, but it is currently infeasible for a user to track the consequences of these inter-relationships with any accuracy, and so they cannot tailor their lifestyle or exercise regime appropriately. In contrast, the system 100, provided with the relevant performance information, can transform the performance information into both progress against the user's targets and also adjustments to those targets, as appropriate.
[0057] The feedback unit 13 can continuously or periodically (or aperiodically) update the metric targets based on the progress information received in connection with other metrics. For example, the feedback unit 13 may update the targets every day, based on the previous day's performance data. The frequency of update may vary from one metric to another. For example, it may be more appropriate to update "exercise" targets more regularly than "nutrition" targets.
[0058] Each metric target may be updated in response to progress against one or more other metrics.
For example, if a "nutrition" target is calculated based on user exercise intensity and BMI, the feedback unit 13 may update that target in response to exercise performance information collected in connection with an "exercise" metric, and also in connection with weight measurements collected in connection with a "biometrics" metric.
[0059] Similarly, the feedback unit 13 serves a second function of updating the schedule
information stored in memory 11, based upon the collected history of the performance information. For example, it could be identified that a user visits a particular location for lunch every day and the feedback unit 11 can update the schedule information accordingly. The particular conditions required to update the schedule information can vary, but could be related, within a margin of error, to the frequency (e.g. daily, weekly or monthly) of a particular activity or type of "performance information" being logged over a predetermined time. For example if performance data indicating that a user cycles in the morning is recorded for more than 8 days out of 10 in a row, a schedule update might be made to include a daily cycle event. Alternatively, if the schedule indicates a user goes swimming every Saturday afternoon, but corresponding performance information is not recorded two weeks out of three in a row, the schedule item could be deleted.
[0060] The server 10 also comprises an interaction determining unit 14, which can also be embodied by a computer processor for example. The interaction determining unit 14 can serve one or both of two functions: firstly, it can be configured to send prompts to the user to record that particular actions have been performed and, secondly, it can provide suggestions of activities that the user could perform. To assist in sending prompts at a relevant time, the server 10 also comprises a clock 15.
[0061] The provision of prompts encourages a user to provide information that is necessary for the system to work effectively (i.e. by making the information providing as complete as possible). As an example, the interaction determining unit 14 might send a prompt in the morning, asking the user to confirm that they have had their normal breakfast, to contribute performance information for a
"nutrition" metric, if the user does not enter the relevant performance information before a particular time. As another example, the interaction determining unit 14 might send a prompt asking a user how they got to work, the response to which could in turn generate performance data regarding the "exercise metric" if a user walked or cycled to work.
[0062] The suggestion function of the interaction determining unit 14 is intended to assist the user in achieving their targets. As such, a suggestion could be given to visit a particular cafe and have a particular meal or drink in order to progress against a "nutrition" target. In some cases, the suggestion could provide the user with options (e.g. the suggestion might mention three menu items for the user to select from). Alternatively, if a user was passing a gym, the interaction determining unit 14 might provide a suggestion to go to the gym and perform a particular set of exercises that would be most appropriate for progressing towards their "exercise" target. The suggestions can take into account the user's schedule. For example, a suggestion to visit a nearby gym might not be issued if the user's schedule has a gym session later in the day.
[0063] The server 10 communicates with a client device 20. The user interfaces with the client device 20 via an input unit or device 21. The input unit 21 could be a keyboard (either integrated with the client device or connected via a cable or wireless connection) or touch-screen if the client device is a smartphone or portable device, for example. Similarly, if the client device 20 is a personal computer, the input unit could incorporate a keyboard and/or a mouse, and/or a microphone provided with voice recognition.
[0064] The client device 20 is also provided with an output unit 22. An output unit can be any means of providing information to the user. As such, if the client device 20 is a smartphone or other portable tablet or computer, the output unit may be a screen and/or a microphone and/or a force feedback/vibration creating device. As such, prompts and suggestions issued by the interaction determining unit 14 could be presented as textual messages on an output 22 display, or could be presented as spoken words via a speaker.
[0065] The client device 20 can optionally also include one or more sensors 23. As such, the client device 20 may be able to directly monitor some parameters relevant to one or more of the wellness metrics, without direct input from the user, or with reduced input. For example, a sensor 23 could be a GPS device, which can be used to monitor a user's movement, which in turn could generate performance information in connection with an "exercise" metric. For example, this could be entirely automatic, based on algorithm to determine a user's speed and therefore likely mode of transportation. Alternatively, a user could confirm that certain journeys recorded via the GPS throughout the day were performed in a certain manner (i.e. through walking or cycling). As another example, the sensor 23 could incorporate an accelerometer, configured to measure the number of steps taken by a user, which could also be sent to the server 20 as "exercise" performance data.
[0066] Whilst it is possible for one or more sensors 23 to be incorporated into the client device 20, it is not necessary. Indeed, a sensor may be present as a separate sensor device 40 (which could include a sensor unit embedded in a larger device). In that scenario, the sensor device 40 could provide information direct to the client device 20 or server 10 regarding the parameters it is measuring, as appropriate. For example, a fully automated sensor that can provide performance information without user input might communicate directly with the server 20. Alternatively, a sensor requiring additional user input before performance information can be generated might communicate with the client device 20.
[0067] The server 10, client device 20, and sensor device 40 can communicate through respective communication unitsl9, 29, 49, via the network 30.
[0068] An non-limitative example of how the system of claim 1 might operate in practice, throughout a day, follows below.
[0069] First, a user might wake up in the morning. A sleep sensor might determine that the user has had eight hours deep sleep and two hours light sleep, for example. This data could be relevant as performance information to an "exercise" metric (which might, for example, incorporate a measure of rest with a measure of activity performed, when creating an overall "exercise" target), or it might contribute to a "biometrics" target. In any case, the performance information is sent to the calculation unit 12, which would calculate the necessary progress against the appropriate targets stored in memory 11.
[0070] The interaction determining unit 14 might then recognise that it is time for breakfast, and suggest a suitable breakfast based on for example, the amount of exercise recorded in the previous day and/or the user's "nutrition" target. The suggestion could be at any level of specificity. For example, it might suggest having a "protein rich" breakfast, or it might suggest having a particular breakfast meal that the user regularly has (and so presumably likes), or might suggest different options (e.g. the three best breakfast options based on the user's targets and performance history).
[0071] The user might then proceed to eat breakfast. They could either proceed to input the information about the breakfast consumed, via a client device 20 which could be a home laptop. Alternatively, the interaction determining unit 14 may send a prompt if it determines that the user's normal breakfast time has passed without any breakfast information being input. The prompt might be picked up by the user on a mobile phone, acting as another client device. The information about the breakfast could be input by selecting a known meal/recipe, which would then be converted in the calculation unit 12 (using the relevant conversion information) into progress information in connection with a "nutrition" metric.
[0072] As it draws close to the user's normal time for leaving for work, the interaction determining unit 14 might suggest a method of commuting. Once again, this could be based upon the user's recent exercise history. Therefore, it could be suggested that a user take a bus if they exercised excessively the day before, or cycle if the user needs to do more exercise and is sufficiently rested.
[0073] The user might then cycle to work. The client device could collect data regarding the distance travelled and the time taken, for example, to derive an average speed and a level of exercise intensity. It could be that this information is recorded in a specific cycling app on a smartphone, for example. In that case there would be no need for the user to provide any additional information about the type of exercise, before the performance information is sent to the calculation unit.
Alternatively, a sensor such as a GPS sensor might monitor the route of a user, and the user might subsequently confirm that the route was cycled.
[0074] The receipt of the cycling performance information at the server 20 might also trigger the feedback unit 13 to update the user's schedule (e.g. because the user has cycled to work every day for the past week) to include cycling to work in the user's schedule. The result of this update could be that, the next morning, the system might not suggest taking the bus (because it expects the user to cycle) and might suggest an alternative breakfast.
[0075] Similarly, it could be determined that the user routinely performs an above average amount of exercise, which in turn could lead to the feedback unit updating the nutrition metric target, to allow for the user requiring more calories due to their elevated exercise routine.
[0076] Also in response to the cycling performance information being received, the interaction determining unit 14 might suggest taking a break from intensive exercise. If the user performs a sedentary job, step and/or accelerometer and/or location sensors might determine that a user does not appreciably move for four hours between arriving at work and lunchtime. As for sleeping, such data might positively contribute towards a "exercise metric" that balances sufficient rest against sufficient activity.
[0077] At lunchtime, the interaction determining unit 14, in response to the time, might suggest a specific lunch for the user. For example, based on the user having performed intensive activity in the form of cycling to work, the interaction determining unit 14 might suggest having a high calorie/high protein meal for lunch. If the user is close to a place having location information stored in memory 11 , the suggestion provided by the interaction determining unit may even specify a particular meal (i.e. a beef sandwich) from a particular nearby shop as being the best meal option locally available. Of course, if the user needs to walk to obtain their lunch, this exercise can be also captured and turned into performance information.
[0078] After eating lunch, the user will input what they consumed through their input unit 21, which will be sent to the server 10 as performance information and processed in the same way as the breakfast information. The interaction determining unit 14 may also suggest a light exercise such as walking after lunch, to aid digestion. Once again, this could generate exercise performance information.
[0079] After lunch, the user may return to their sedentary job and once again record no activity until they leave work. However, whilst this may generate performance information, that information might not provide any progress against the exercise metric if the user has already had enough "rest" that day, according to the "exercise" target.
[0080] After work, the user might walk to the gym. The interaction determining unit 14 might determine that, based on GPS information from a sensor 23 or 40 and the schedule information in the memory 11, the user is heading towards the gym. As such, it might issue a suggestion that the user has a light snack before they start training. If the user acts on the suggestion, and inputs the relevant information, that information will be turned into performance information recorded against the "nutrition" target.
[0081] Upon reaching the gym, the interaction determining unit 14 might determine that a user has done enough cardiovascular exercise that day (e.g. through cycling to work) and therefore suggests that the user focuses on muscle-building activities whilst at the gym.
[0082] The user might choose to ignore the suggestion to focus on muscle-building, and instead spend time at the gym running. This could generate performance information which reduces the users progress towards their "exercise target", if they have already done enough cardiovascular exercise that day. Such "negative" performance information could also cause the interaction determining unit to issue a suggestion to eat additional carbohydrates in order to compensate for the over-exercise, for example.
[0083] The user might go home for dinner, but forget to enter the details of their dinner into the system 100. Therefore, later that night, the interaction determining unit 14 might issue a prompt asking the using to input the relevant information about their evening meal.
[0084] Before the user goes to bed, the interaction determining unit 14 might issue a suggestion that the user performs some light exercise such as stretching or yoga to avoid stiffness after the exercise- intensive day. Performing this activity could generate performance information relating to the parameters of the number of calories burned and also the type of activity performed. For example, the exercise metric might measure whether yoga is performed every day, and allocate progress towards the "exercise" target based on the fact that some (i.e. any) yoga has been performed. That is, one aspect of the "exercise" metric could be a "check list" of different types of exercise that should be performed throughout the day.
[0085] The interaction determining unit 14 may also suggest that the user weighs themselves before going to bed. The system 100 could interact directly with "smart scales" acting as a sensor device 40, or the user could input their weight information through the input unit 21. Irrespective of the way the information is recorded, it could contribute performance information towards a user's "biometrics" target, for example. That is, the biometrics "metric" might also operate as a "check list" for activities such as a user weighing themselves or taking their blood pressure and heart rate, or performing activities such as taking blood tests (e.g. if a user has diabetes for example).
[0086] As will be apparent from the preceding discussion, the system 100 can provide various types of prompts and suggestions based upon the user's activity history. Importantly, however, the system is also able to "learn" from the user's habits. [0087] As such, the system can adapt to the requirements of the user and provide a more personalised experience. This in turn makes it more likely that the user will remain motivated, as the prompts and suggestions will be appropriate to them, and so less likely to become an annoyance which the user will wish to simply ignore rather than respond to.
[0088] Fig. 2 shows the operation of aspects of the wellness system in terms of a steps in a flowchart.
[0089] At step S201, the system 100 is initiated. At this stage a user might input basic or "static" information about themselves (or non-static information that will subsequently be updated by the system) and select the metrics they wish to use.
[0090] At step S202, the targets for the various metrics are calculated and stored. In addition, schedule information is obtained and stored (for example from an electronic calendar) if it is available.
[0091] At step S203, performance information is received. The performance information could be received directly/automatically from a sensor 23,40 or could be the result of user input through an input device 21.
[0092] The performance information is processed in steps S204 and S205, which may occur concurrently.
[0093] At step S204 the performance information is used, for example by the calculation unit 12 to calculate progress against the targets. This may involve use of conversion information, as discussed previously. The progress may then be sent for output at step S206.
[0094] At step S205 the performance information obtained in step S203 is used to update the user's schedule information and/or their targets. As discussed above, if the performance information represents a particular type/amount of exercise, schedule information could be updated to include that exercise every day (if this is determined to be appropriate based on the performance information history) and the target for a "nutrition" metric could be updated to increase the number of calories a user should consume. This updating can be done in response to the incoming 'raw' performance information or the 'processed' progress against the metrics, as appropriate (e.g. it may be appropriate in the preceding example to update the schedule based on the raw information about the type of exercise, whilst the "nutrition" metric might be updated on the processed progress against the relevant target) .
[0095] The skilled reader will understand that variations of the specific embodiments described are possible. The invention can be implemented in many forms, including as a computer program performing the method described, or as storage medium storing instructions in the form of computer readable code for implementing the method described. As such, the forgoing description is not intended to limit the invention. The invention is defined in the appended claims.

Claims

A system for monitoring and measuring wellness, the system comprising:
a memory configured to store targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics;
an input unit for providing performance information about the user relating to at least a first one of the wellness metrics;
a calculation unit configured to calculate an updated progress against the first wellness metric based on the provided performance information, and
a feedback unit configured to update the target for a wellness metric, based on the provided performance information relating to the first wellness metric.
The system for monitoring and measuring wellness according to claim 1, wherein the feedback unit is configured to update the target for a second wellness metric, based on the provided performance information relating to the first wellness metric.
The system for monitoring and measuring wellness according to claim 1 or claim 2, the system further comprising: a sensor for collecting the performance information, or precursor data to the performance information, about at least the first metric.
The system for monitoring and measuring wellness according to claim 3, wherein the sensor comprises a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
The system for monitoring and measuring wellness according to any preceding claim, further comprising an output unit for outputting the progress against the targets for the wellness metrics.
The system for monitoring and measuring wellness according to claim 5, wherein the output unit is a display or a speaker.
7. The system for monitoring and measuring wellness according to claims 5 or 6, wherein:
the memory is further configured to store user schedule information; and
an interaction determining unit is configured to provide an input prompt to the user, via the output unit and based on the schedule information, requesting input relating to metric-specific performance data.
8. The system for monitoring and measuring wellness according to claim 7, wherein the interaction determining unit is further configured to provide the input prompt based on time and/or location of the user.
9. The system for monitoring and measuring wellness according to any preceding claim, wherein: the feedback unit is further configured to update the schedule information based upon a history of the performance information.
10. The system for monitoring and measuring wellness according to claim 9, wherein:
the feedback unit is further configured to update the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations.
11. The system for monitoring and measuring wellness according to any preceding claim, wherein the system further comprises a clock and/or user location determining unit; and
the interaction determining unit is further configured to provide a suggestion, via the output unit and based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
12. The system for monitoring and measuring wellness according to any preceding claim, wherein: the memory is further configured to store location information, the location information including a place and information related to at least one of the wellness metrics; and
the interaction determining unit, when the user location determining unit determines that a user is within a predetermined distance of a place recorded in the location information, is further configured to provide a suggestion based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target.
13. The system for monitoring and measuring wellness according to any preceding claim, wherein: the memory is further configured to store conversion information, for converting performance information into progress against one or more targets; and
the calculation unit is configured to calculate the updated progress from the performance information with reference to the conversion information.
14. A method of monitoring and measuring wellness, the method comprising:
storing targets for a user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics;
receiving performance information about the user relating to a first one of the wellness metrics;
calculating an updated progress against the first wellness metric based on the provided performance information, and
updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric.
15. The method of monitoring and measuring wellness according to claim 14, wherein updating the target for a wellness metric, based on the provided performance information relating to the first wellness metric, comprises updating the target for a second wellness metric.
16. The method of monitoring and measuring wellness according to claim 14 or 15, the method further comprising: collecting, with a sensor, the performance information, or precursor data to the performance information, about at least the first metric.
17. The method of monitoring and measuring wellness according to claim 16, wherein the sensor comprises a GPS locator, an accelerometer, a heart rate detector, blood pressure detector, a breathing rate detector, a step counter, an odometer, medical analysis device and/or scales for measuring weight.
18. The method of monitoring and measuring wellness according to any preceding method claim, further comprising outputting the progress against the targets for the wellness metrics.
19. The method of monitoring and measuring wellness according to claim 18, wherein the outputting is performed via a display or a speaker.
20. The method of monitoring and measuring wellness according to any preceding method claim, further comprising:
storing user schedule information; and
providing an input prompt to the user, based on the schedule information, requesting input relating to metric-specific performance data.
21. The method of monitoring and measuring wellness according to claim 20, wherein the input prompt is based on time and/or location of the user.
22. The method of monitoring and measuring wellness according to claims 20 or 21, further
comprising:
updating the schedule information based upon a history of the performance information.
23. The method of monitoring and measuring wellness according to claim 22, further comprising: updating the schedule information if similar performance information is repeatedly recorded at similar times and/or in similar locations.
24. The method of monitoring and measuring wellness according to any one of claims 20-23, further comprising:
providing a suggestion, based on the schedule information and the progress of the user towards at least one of the targets, the suggestion relating to an activity for further progressing towards the at least one target.
25. The method of monitoring and measuring wellness according to claim 24, further comprising: storing location information, the location information including a place and information related to at least one of the wellness metrics; and
providing a suggestion, when it is determined that a user is within a predetermined distance of a place recorded in the location information, based on the location information for that place and the progress of the user towards at least one of the targets, the suggestion relating to attaining the at least one target.
26. The method of monitoring and measuring wellness according to any preceding method claim, further comprising:
storing conversion information, for converting performance information into progress against one or more targets; and
calculating the updated progress from the performance information with reference to the conversion information.
27. A storage medium storing computer readable code for implementation by a computer or network of computers, the code, when implemented, causing the computer or network of computers to implement the steps of any one of the preceding method claims.
A computerised system for monitoring and measuring wellness, the system comprising:
a memory configured to store targets for the user in connection with two or more wellness metrics, progress of the user towards each target, and a history of user-specific performance information relating to the wellness metrics;
an input device for providing performance information about the user relating to a first one of the wellness metrics;
a processor configured to calculate an updated progress against the first wellness metric based on the provided performance information, and
wherein the processor is further configured to update the target for a second metric, based on the provided performance information relating to the first wellness metric.
A system for method of monitoring and measuring wellness, constructed and arranged substantially as hereinbefore described or as illustrated in any one of the accompanying drawings.
30. A method of monitoring and measuring wellness as hereinbefore described or as illustrated in any one of the accompanying drawings.
PCT/GB2015/052095 2014-07-18 2015-07-20 Wellness system WO2016009229A1 (en)

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Citations (4)

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US20110095916A1 (en) * 2006-07-10 2011-04-28 Accenture Global Services Limited Mobile Personal Services Platform for Providing Feedback
WO2008036275A2 (en) * 2006-09-21 2008-03-27 Apple Inc. Dynamically adaptive scheduling system
US20140115008A1 (en) * 2007-02-16 2014-04-24 Bodymedia, Inc. Using aggregated sensed data of individuals to predict whether an individual requires assistance from a responder
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