FIELD OF INVENTION

The invention relates to exercise and/or activity monitoring and in particular to classification of exercise or activity measurement data.

BACKGROUND

Devices for monitoring physiological information such as heart rate, and other exercise related information such as speed and distance exist. Such devices provide a means of representing the quality of the exercise conducted by a user wearing the device or exercising on a machine incorporating the device. It is often difficult for the user to effectively interpret this representation of quality or use it to compare it against their goals. The state of the art has therefore developed further to provide systems compatible with a particular measurement device for downloading and uploading exercise data to and from the device for processing, with the object of making the data more meaningful for the user. For example, systems are available which enable a user to upload a workout program onto a wearable exercise device. The user exercises according to the program while the device monitors the user's heart rate. Once the exercise is complete, the user downloads the monitored data to a computer to process the data in some cases against the workout program and provide statistics on that particular exercise session.

In the case of exercise, in at least some cases automated exercise devices are rigid in their approach to a user exercising. For example, a preloaded training workout may stipulate a 2 minute warm up followed by a 4 minute interval at a speed close to half marathon race pace followed by easy recovery running for 3 minutes before another 4 minute half marathon race pace effort. There are two fundamental problems with this. Firstly the user may reach a hill just before he/she starts the 4 minute half marathon race pace effort and have to run it up the hill. The user may then end up having to do the second 4 minute half marathon race pace effort just as they reach the crest of the hill and descend. This rigidity of set training for each activity within the workout or activity can mean that the user experience is poor and that the data obtained will have far greater variability and error within it, potentially making it almost meaningless to analyse and to draw conclusions from. Secondly without understanding the full context of the workout it is difficult for a trainer for example to comment effectively, without for example knowing the terrain or the resistance that the user is experiencing and this can mean interpretation may be reduced to guesswork.

Current systems can also be heavily dependent on the type of monitoring device used as they are generally limited to monitoring specific parameters e.g. heart rate for interpretation of the exercise. Furthermore, interpretation of exercise data in at least some current systems is dependent on pre-established assumptions of the user's exercise regime. In other words, the exercise data is processed with the assumption that the user is performing a specific type of exercise. These factors limit the diversity of the systems and in some cases their accuracy, should the user choose to divert from the type of exercise specified by a particular program. Lastly, the statistics provided by the system may still be meaningless to an unskilled user and would generally require the aid of a specialised trainer to interpret them and provide advice/guidance as to how to modify the user's exercise program so the user can meet their goals.

Exercise and activity devices that measure biometric and environmental data such as heart rate, speed, leg or arm turnover or stroke rate, altitude, temperature, R-R, power, slope, distance per turnover, location, distance and time currently exist. This data is displayed on a watch or device screen or spoken through headphones. These systems are merely measurement devices. This means that once the activity data is collected, the user must have the relevant level of skill to analyze and interpret it and then decide what changes they should make to their future exercise to optimize their time and effort during training and to maximize improvements.

FIG. 3for example shows data downloaded from a measurement device. It is very difficult to extract any clear understanding or information from such raw data. Hundreds of millions of people around the world exercise ineffectively due to poor understanding of the appropriate strategies to maximizing fitness, sports performance and health improvements through activity. In most cases they do not have access to a coach.

In most cases, users ultimately do not want data from a measuring device, they want to know what was correct about what they did, what problems and solutions they need to work on and what to do next. They need someone or something to interpret the data and provide intelligent feedback.

When tracking a soldier in the field or a user engaging in health related activities throughout a full day, it has been difficult in the past to clearly establish different activities that have occurred during the day, and/or assess levels of fatigue or areas of weakness.

It is an object of the present invention to provide a method and system for classifying exercise and/or activity related data into different activities to provide an accurate representation of a particular exercise session or form of activity, or to at least provide the public with a useful choice.

SUMMARY OF THE INVENTION

In a first aspect the invention may broadly be said to consist of a method for analysing an activity session comprising:receiving activity data over a period of time or distance indicative of one or more activities performed during the activity session, said data relating to multiple parameters monitored during the activity session,andutilising a classification system to identify the one or more activities performed during the activity session based on the received activity data, a classification system defining an activity based on a set of threshold criteria for a combination of multiple parameters.

Preferably the data is received in multiple data streams of consecutive data points, each stream relating to one of said monitored parameters, and utilising said classification system comprises:a) comparing corresponding data points of two or more data streams against the set of threshold criteria for one or more activities,b) identifying when the corresponding data points satisfy the threshold criteria for an activity and if satisfied classifying the corresponding data points under the activity, andc) repeating steps a) and b) for other data points from the data streams to identify the one or more activities performed during the activity session.

Preferably the step of utilizing the classification system further comprises grouping consecutive data points classified under the same activity to define an instance of the activity within the activity session, the number of consecutive data points being indicative of duration of the instance of the activity.

Preferably the step of utilizing the classification system is performed upon or after receiving the activity data for the entire activity session.

Alternatively the activity data is received during the activity session and the step of utilizing the classification system is performed simultaneously during the activity session.

The activity data may be time stamped by a user during an activity session to denote an instance of an activity and said method comprises receiving one or more time stamped blocks of activity data and utilizing the classification system to identify an activity performed during one or more of the time stamped blocks.

Preferably the multiple parameters monitored are any combination of two or more of: resistance, effort, location, turnover, distance covered per turnover, positional status, ECG, blood pressure, ambient temperature, relative humidity, barometric pressure, heat index, local wind speed, local wind direction, local rain, body weight, carried weight, vertical jumping height, horizontal jumping distance, drop, climb, crawl, heading direction, oxygen uptake, respiration, ventilation, energy expenditure, energy intake, R-R (HRV), body temperature, weather condition, degree of movement, ground status, and direction user facing.

Preferably the combination of monitored parameters for defining an activity includes a measure of resistance or change in resistance and a measure of effort or change in effort.

The measure of effort or change in effort may be a measure of speed or change in speed, a measure of heart rate or change in heart rate, or a measure of power or change in power, or any combination thereof.

The measure of resistance or change in resistance may be a measure of altitude or change in altitude, a measure of slope or change in slope, or a measure of gradient or change in gradient, or any combination thereof.

Alternatively or in addition, the measure of resistance or change in resistance may be a determined from stride rate or change in stride rate, stroke rate or change in stroke rate, or cadence or change in cadence, or any combination thereof.

Preferably the classification system comprises any one or more of a weight loss activity classification, an activity status monitoring classification, a running classification, a cycling classification, a rowing/kayaking classification, a field sports classification, a pedometer activity monitoring classification, and a horse training classification.

Preferably the data relating to the parameters monitored during the activity session is obtained from one or more activity monitoring devices.

Preferably the method further comprises processing data classified under an activity to determine a level of compliance with a predetermined training plan.

Preferably the method further comprises updating a training plan based on the level of compliance.

In a second aspect the invention may broadly be said to consist of a system for analysing an activity session comprising a classification module arranged to receive activity data indicative of one or more activities performed during the activity session, said data relating to multiple parameters monitored during the activity session and having:at least one memory component for storing one or more classifications for defining one or more activities, each activity being defined based on a set of threshold criteria for a combination of multiple parameters, andat least one processor arranged to utilise the classifications to process the received activity data and identify one or more activities performed during the activity session.

Preferably the classification module is arranged to receive the data in multiple data streams of consecutive data points, each stream relating to one of said monitored parameters, and said processor is arranged to utilize the one or more classifications by:a) comparing corresponding data points of two or more data streams against the set of threshold criteria for one or more activities,b) identifying when the corresponding data points satisfy the threshold criteria for an activity and if satisfied classifying the corresponding data points under the activity, andc) repeating steps a) and b) for other data points from the data streams to identify the one or more activities performed during the activity session.

Preferably the processor is further arranged to group consecutive data points classified under the same activity to define an instance of the activity within the activity session, the number of consecutive data points being indicative of a duration of the instance of the activity.

Preferably the processor is arranged to process the activity data upon or after receiving the activity data for the entire activity session.

Alternatively the processor is arranged to process the activity data upon receiving activity data during the activity session.

The data may be received in time stamped blocks and the processor may be arranged to utilize the one or more classifications to process one or more of the time stamped blocks of data and identify an activity performed during each of the one or more blocks.

Preferably the multiple parameters monitored can be any combination of two or more of: resistance, effort, location, turnover, distance covered per turnover, positional status, ECG, blood pressure, ambient temperature, relative humidity, barometric pressure, heat index, local wind speed, local wind direction, local rain, body weight, carried weight, vertical jumping height, horizontal jumping distance, drop, climb, crawl, heading direction, oxygen uptake, respiration, ventilation, energy expenditure, energy intake, R-R (HRV), body temperature, weather condition, degree of movement, ground status, and direction user facing.

Preferably the combination of monitored parameters for defining an activity includes a measure of resistance or change in resistance and a measure of effort or change in effort.

The measure of effort or change in effort may be a measure of speed or change in speed, a measure of heart rate or change in heart rate, or a measure of power or change in power, or any combination thereof.

The measure of resistance or change in resistance may be a measure of altitude or change in altitude, a measure of slope or change in slope, or a measure of gradient or change in gradient, or any combination thereof.

Alternatively or in addition, the measure of resistance or change in resistance may be determined from stride rate or change in stride rate, stroke rate or change in stroke rate, cadence or change in cadence, or any combination thereof.

Preferably the memory component has stored therein any one or more of a weight loss activity classification, an activity status monitoring classification, a running classification, a cycling classification, a rowing/kayaking classification, a field sports classification, a pedometer activity monitoring classification, and a horse training classification.

Preferably the system further comprises one or more activity monitoring devices, each arranged to obtain data indicative of parameters monitored during an activity session.

Preferably the classification module is remote from the one or more monitoring devices and each monitoring device is arranged to transmit the data indicative of the monitored parameters to the classification module.

Preferably the system further comprises:

a central station for accommodating the classification module, and

a receiver for receiving data indicative of multiple parameters monitored during an activity session from the one or more monitoring devices.

Alternatively the classification module is housed within each monitoring device.

Preferably the system further comprises an interpretation module having a processor for processing data classified under an activity to determine a level of compliance with a predetermined training plan.

Preferably the system further comprises a plan generation module having a processor for updating a training plan based on the level of compliance determined by the interpretation module.

In a third aspect the invention may broadly be said to consist of a classification system for analysing activity data relating to multiple parameters monitored during an activity session to determine one or more activities performed during the activity session, the classification system comprising:at least one memory component for storing one or more classifications for defining one or more activities, each activity being defined based on a set of threshold criteria for a combination of parameters, andat least one processor arranged to utilise the classifications to process the activity data and identify one or more activities performed during the activity session.

Preferably the classification system is arranged to receive the data in multiple data streams of consecutive data points, each stream relating to one of said monitored parameters, and said processor is arranged to utilize the one or more classifications by:a) comparing corresponding data points of two or more data streams against the set of threshold criteria for one or more activities,b) identifying when the corresponding data points satisfy the threshold criteria for an activity and if satisfied classifying the corresponding data points under the activity, andc) repeating steps a) and b) for other data points from the data streams to identify the one or more activities performed during the activity session.

In this specification activity or activity type can mean any type of action performed by an individual or group of individuals over a period of time or distance (or both) which may or may not involve movement, such as lying/sitting down and running/cycling. The term is intended to cover general activities such as running as well as specific activities such as running uphill at a certain pace. An activity session or workout means a period of time where an individual performs one or more activities. Exercise and exercise sessions (or workout) are intended to be covered by the terms activity and activity sessions respectively. Activity period refers to the period within an activity session in which an activity is performed. Training type is also intended to fall within the meaning of activity. Training zone is intended to mean a threshold zone or criteria for a single parameter that defines an aspect of an activity. An activity is composed of training zones from multiple parameters.

The term “comprising” as used in this specification and claims means “consisting at least in part of”. When interpreting each statement in this specification and claims that includes the term “comprising”, features other than that or those prefaced by the term may also be present. Related terms such as “comprise” and “comprises” are to be interpreted in the same manner.

The invention consists in the foregoing and also envisages constructions of which the following gives examples only.

