Calculating pace and energy expenditure from athletic movement attributes

• US 10,802,038 B2
• Filed: 10/14/2014
• Issued: 10/13/2020
• Est. Priority Date: 10/14/2013
• Status: Active Grant

• Current Assignee: Nike, Inc.
• Original Assignee: Nike, Inc.
• Inventors: Balakrishnan, Santoshkumar, Goel, Manan, Wilkins, Bradley W., Dow-Hygelund, Corey, Hazel, Jeff, Schmitt, John
• Primary Examiner(s): Abouelela, May A
• Application Number:
  US14/514,093
• Publication Number:
  US 20160213974A1
• Time in Patent Office: 2,191 Days
• Field of Search: 600300, 600587, 600595, 600481, 434127, 345419
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• CPC Class Codes: A61B 5/6807 Footwear
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6898 : Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7235 : Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A63B 2024/0015 : Comparing movements or motion sequences with computerised simulations of movements or motion sequences, e.g. for generating an ideal template as reference to be achieved by the user
• CPC Class Codes: A63B 2024/0068 : Comparison to target or threshold, previous performance or not real time comparison to other individuals
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 24/0006 : Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G01B 21/00 : Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
• CPC Class Codes: G01P 13/00 : Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G01P 15/00 : Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01P 15/18 : in two or more dimensions
• CPC Class Codes: G06F 1/163 : Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 2218/14 : by matching peak patterns
• CPC Class Codes: G06F 3/011 : Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/014 : Hand-worn input/output arrangements, e.g. data gloves
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06V 40/25 : Recognition of walking or running movements, e.g. gait recognition
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

Systems and methods configured to process motion data associated with a user. The systems and methods are configured to receive motion data from a sensor, calculate motion attributes from the data, and classify the motion data using one or more mathematical models. Attributes may be calculated without classifying the motion data into an activity. Attributes may be compared to mathematical models. Motion data within the models and attributes of the user may be independent of any activity type. Attributes may be compared to select an energy expenditure model from one or more energy expenditure models, or an activity classification model, from the one or more activity classification models. An energy expenditure, or a classification of received data as a linear travel motion, may then be calculated.

Claims

1. A unitary apparatus configured to be worn on a user, comprising:

a processor;

a sensor configured to capture motion data of the user;

a non-transitory computer-readable medium comprising computer-executable instructions that when executed by the processor perform at least;

receiving, from the sensor and via one or more wireless networks, while being worn by the user, a data stream comprising one or more data points generated as a result of a motion of the user;

transforming the one or more data points into a dataset representing a motion of the user;

without classifying the one or more data points into an activity type, calculating a first set of one or more motion attributes from the dataset;

determining, for each model of one or more models, a probability that each model is a best-match to the first set of one or more motion attributes based on a regression function and a comparison of the first set of one or more motion attributes from the dataset to a second set of one or more motion attributes in each model of the one or more models, wherein the second set of one or more motion attributes and the first set of one or more motion attributes are independent of any activity type, and wherein the first set and the second set have a same number of motion attributes;

selecting, based on the regression function and a result of the comparison of the first set of one or more motion attributes from the dataset to the second set of one or more motion attributes of the one or more models, a first model, from the one or more models, as a best-match to the first set of one or more motion attributes from the dataset, wherein the first model is selected as the best-match based on a determination that the first model is associated with a highest probability of each of the probabilities; and

inputting, into the first model, the first set of one or more motion attributes from the dataset;

receiving, from the first model, an output indicating one or more characteristics associated with the motion of the user.

2. The unitary apparatus of claim 1, wherein the one or more models comprise one or more energy expenditure models and one or more activity classification models.

3. The unitary apparatus of claim 2, wherein an energy expenditure model, selected from the one or more energy expenditure models, is utilized to calculate an energy expenditure characteristic associated with the motion of the user.

4. The unitary apparatus of claim 2, wherein an activity classification model, selected from the one or more activity classification models, is utilized to classify the motion of the user as a linear travel motion.

5. The unitary apparatus of claim 4, wherein the activity classification model further classifies the linear travel motion as running or walking.

6. The unitary apparatus of claim 4, wherein the activity classification model, selected from the one or more activity classification models, calculates a pace of the user.

7. The unitary apparatus of claim 1, wherein the non-transitory computer-readable medium comprises instructions that when executed by the processor further perform at least:

validating the one or more data points by comparison to one or more threshold values.

8. The unitary apparatus of claim 1, wherein the data points generated by the user'"'"'s motion are generated during a performance of a first activity type, and wherein at least one of the one or more models is devoid of motion data collected during any individual'"'"'s performance of the first activity type.

9. The unitary apparatus of claim 1, wherein the first set of one or more motion attributes are calculated as omnidirectional attributes from sensor data that includes a value for each of an x-axis, a y-axis, and a z-axis.

10. The unitary apparatus of claim 1, wherein the first set of one or more motion attributes are calculated as unidirectional attributes from sensor data that includes a value for one of an x-axis, a y-axis, or a z-axis.

11. The unitary apparatus of claim 1, wherein the first set of one or more motion attributes are calculated from a combination of one or more data points captured from the sensor and biographic data related to the user.

12. The unitary apparatus of claim 3, wherein the non-transitory computer-readable medium further comprises instructions that when executed by the processor further perform at least:

after selecting the energy expenditure model from the one or more energy expenditure models, categorizing the motion data of the user into an activity type.

13. The unitary apparatus of claim 1, wherein the sensor is a first sensor comprising an accelerometer, and wherein the non-transitory computer-readable medium further comprises instructions that when executed by the processor further perform at least:

receiving heart rate data obtained from the user during the user'"'"'s performance of the motion detected by the sensor; and

determining to cease utilizing heart rate data despite being accurate based upon the accelerometer providing data indicative that the user has reduced their physical activity below a threshold amount for at least a first time frame.

14. The unitary apparatus of claim 1, wherein the capturing from the sensor is conducted while being worn on an appendage of the user.

15. A computer-implemented method, comprising:

receiving, by one or more processors, from a sensor, a data stream comprising one or more data points generated as a result of a motion of a user;

transforming, by the one or more processors, the one or more data points into a dataset representing a motion of the user;

without classifying the one or more data points into an activity type, calculating, by the one or more processors, a first set of one or more motion attributes from the dataset;

determining, by the one or more processors, for each model of one or more models, a probability that each model is a best-match to the first set of one or more motion attributes based on a regression function and a comparison of the first set of one or more motion attributes from the dataset to a second set of one or more motion attributes in each model of the one or more models, wherein the second set of one or more motion attributes and the first set of one or more motion attributes are independent of any activity type, wherein the first set and the second set have a same number of motion attributes;

selecting, by the one or more processors, based on the regression function and a result of the comparison of the first set of one or more motion attributes from the dataset to the second set of one or more motion attributes of the one or more models, a first model, from the one or more models, as a best-match to the first set of one or more motion attributes from the dataset, wherein the first model is selected as the best-match based on a determination that the first model is associated with a highest probability of each of the probabilities;

inputting, by the one or more processors, into the first model, the first set of one or more motion attributes from the dataset; and

receiving, by the one or more processors, from the first model, an output indicating one or more characteristics associated with the motion of the user.

16. The computer-implemented method of claim 15, wherein the one or more models comprise one or more energy expenditure models and one or more activity classification models.

17. The computer-implemented method of claim 16, wherein an activity classification model, selected from the one or more activity classification models, is utilized to classify the motion of the user as a linear travel motion.

18. The computer-implemented method of claim 15, wherein the data points generated by the user'"'"'s motion are generated during a performance of a first activity type, and wherein at least one of the one or more models is devoid of motion data collected during any individual'"'"'s performance of the first activity type.

19. The computer-implemented method of claim 15, wherein the first set of one or more motion attributes are calculated as either:

(a) omnidirectional attributes from sensor data that includes a value for each of an x-axis, a y-axis, and a z-axis, or (b) unidirectional attributes from sensor data that includes a value for one of an x-axis, a y-axis, or a z-axis.

20. The computer-implemented method of claim 15, wherein the first set of one or more motion attributes are calculated from one or more data points representing variation between the motion data point outputs from the sensor.