Calculating pace and energy expenditure from athletic movement attributes

• US 10,422,810 B2
• Filed: 10/14/2014
• Issued: 09/24/2019
• 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): Huynh, Phuong
• Application Number:
  US14/513,916
• Publication Number:
  US 20150106052A1
• Time in Patent Office: 1,806 Days
• 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. Further, the systems and methods are configured to identify and quantify, using the one or more mathematical models, the motion data as linear travel motion associated with the user running or walking.

Claims

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

a processor;

a sensor configured to capture motion data of the user;

a communication element configured to communicate with a remote device; and

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

capturing, from the sensor while being worn on an appendage of the user, a data stream comprising one or more data points generated by the sensor as a result of a motion of the user;

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

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

calculating one or more motion attributes from the dataset;

utilizing the one or more motion attributes as input values to an activity classification model, wherein the activity classification model classifies the motion of the user as a linear travel motion or another form of motion; and

transmitting, via the communication element and to the remote device, the one or more motion attributes.

2. The unitary apparatus of claim 1, wherein the one or more motion attributes used to classify the motion as the linear travel motion are utilized as input values to a linear motion model.

3. The unitary apparatus of claim 2, wherein the linear motion model classifies the motion of the user as running or walking.

4. The unitary apparatus of claim 2, wherein the linear motion model calculates a pace of the user.

5. The unitary apparatus of claim 1, wherein the sensor comprises an accelerometer.

6. The unitary apparatus of claim 5, wherein the one or more threshold values correspond to one or more of x-, y-, and z-axis data outputs from the accelerometer.

7. The unitary apparatus of claim 5, wherein the one or more threshold values are compared to a highest magnitude output from one or more of x-, y-, and z-axis data outputs from the accelerometer.

8. An apparatus, comprising:

a processor;

a sensor configured to output data in response to a motion of a user;

a communication element configured to communicate with a remote device; and

a non-transitory computer-readable medium comprising computer-executable instructions, that when executed by the processor, are configured to cause the apparatus at least to;

capture, from the sensor, one or more data points generated as a result of a motion of the user;

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

calculate one or more motion attributes from the dataset; and

classify the motion of the user as a linear travel motion by inputting the one or more motion attributes into a linear motion model; and

transmit, via the communication element and to the remote device, the one or more motion attributes.

9. The apparatus of claim 8, wherein the sensor is an accelerometer configured to output acceleration data for one or more of an x-axis, a y-axis, and a z-axis.

10. The apparatus of claim 9, wherein the linear motion model compares the acceleration data to one or more threshold values.

11. The apparatus of claim 10, wherein the one or more threshold values are compared to a highest magnitude output from one or more of the x-axis, y-axis, and z-axis data outputs from the accelerometer.

12. The apparatus of claim 8, wherein the linear travel motion corresponds to the user running.

13. The apparatus of claim 8, wherein the linear travel motion corresponds to the user walking.

14. The apparatus of claim 8, wherein the non-transitory computer-readable medium further comprises computer-executable instructions, that when executed by the processor, are further configured to cause the apparatus at least to:

calculate, using the linear motion model, a speed of the user.

15. The apparatus of claim 9, wherein the one or more motion attributes are selected from the group consisting of:

a trimmed mean of a vector normal, a derivative of a trimmed mean of a vector normal, and an average value of the acceleration data from the one or more of the x-, y-, and z-axes.

16. The apparatus of claim 9, wherein the one or more motion attributes are selected from the group consisting of:

a vector normal calculated as a square root of a sum of squares of x-axis, y-axis, and z-axis values;

a range of x-axis values;

an arm swing frequency;

a trimmed mean of x-axis values;

an interquartile range of z-axis values;

an arm swing power;

a skew;

an integral of x-axis values;

a step frequency determined from a vector normal using a time domain frequency;

a summation of max of quartered windows;

a standard deviation of a derivative;

a sum of a standard deviation of x-axis, y-axis, and z-axis values;

an integral of a vector normal;

a step frequency power;

a mean of a standard deviation values of quartered windows;

a maximum of a vector normal;

a median absolute deviation of a derivative of a vector normal;

an interquartile range of x-axis values;

a trimmed mean of a vector normal;

an absolute value of a median data point of x-axis data;

a median absolute deviation of data associated with x-axis;

a standard deviation of x-axis data;

an integral of y-axis values;

an integral of z-axis data;

a range of z-axis data;

a minimum value of z-axis data;

an interquartile range of data from the x-axis, y-axis, and z-axis;

a sum of a median absolute deviation of data from the x-axis, y-axis, and z-axis;

a sum of a median of data from x-axis, y-axis, and z-axis;

a maximum derivative of a vector normal;

a minimum derivative of a vector normal;

an absolute trimmer mean of a derivative of a vector normal;

a standard deviation of a vector normal;

one or more zero crossings of vector normal data;

a skew of vector normal data;

an interquartile range of vector normal data; and

a standard deviation of x-axis values.

17. A non-transitory computer-readable medium comprising computer-executable instructions that when executed by a processor are configured to perform at least:

capturing, from a sensor, one or more data points generated as a result of a motion of a user;

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

calculating one or more motion attributes from the dataset;

inputting the one or more motion attributes to a linear motion model;

comparing the one or more motion attributes to one or more threshold values;

classifying the motion of the user as a linear travel motion; and

transmitting, via a communication element and to a remote device, the one or more motion attributes.

18. The non-transitory computer-readable medium of claim 17, wherein the sensor is an accelerometer configured to output acceleration data for one or more of an x-axis, a y-axis, and a z-axis.

19. The non-transitory computer-readable medium of claim 18, wherein the one or more threshold values are compared to a highest magnitude output from one or more of the x-axis, y-axis, and z-axis data outputs from the accelerometer.

20. The non-transitory computer-readable medium of claim 17, further comprising computer-executable instructions, that when executed by the processor, are further configured to perform at least:

calculating, using the linear motion model, a running speed of the user.