Head Worn Sensor Device and System for Exercise Tracking and Scoring

• US 20150100141A1
• Filed: 10/06/2014
• Published: 04/09/2015
• Est. Priority Date: 10/07/2013
• Status: Abandoned Application

• Current Assignee: Zinc Software Limited
• Original Assignee: Zinc Software Limited
• Inventors: Hughes, Darran John
• Application Number:
  US14/506,971
• Publication Number:
  US 20150100141A1
• Time in Patent Office: Days
• US Class Current: 700/92
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• 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 5/6814 Head
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• 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

The subject invention is directed to a computer-implemented method, a device and a system for detecting and scoring exercises, including maintaining, by a mobile device, a library of motion signatures, in which a motion signature for an exercise is a sequence of characteristic features and a characteristic feature is a movement in the exercise; receiving, by a mobile device, a time series of data from a sensor device, the sensor device attached to the head or torso of a user, the data comprising accelerometer data from an accelerometer included in the sensor device, detecting a single repetition of a designated exercise performed by the user and calculating a motion score for the single repetition of the detected exercise.

Claims

1. A computer-implemented method for detecting and scoring exercises, comprising:

maintaining, by a mobile device, a library of motion signatures, wherein a motion signature for an exercise comprises a sequence of characteristic features and wherein a characteristic feature is a movement in the exercise;

receiving, by a mobile device, a time series of data from a sensor device, the sensor device attached to the head or torso of a user, the time series of data comprising accelerometer data from an accelerometer included in the sensor device;

detecting a single repetition of a designated exercise performed by the user, wherein detecting comprises;

segmenting the time series of accelerometer data into a sequence of characteristic features as specified by the motion signature that corresponds to the designated exercise; and

calculating a motion score for the single repetition of the detected exercise.

2. The method of claim 1 wherein detecting further comprises validating that the range of values in each segment of accelerometer data is within a specified range of values.

3. The method of claim 1 where the received time series of data further comprises data from a photoplethysmograph included in the sensor device, the method further comprising:

calculating a heart rate for the user; and

calculating a heart rate score for the designated exercise.

4. The method of claim 3 wherein the heart rate score is based on the increase in the user'"'"'s heart rate while performing the designated exercise.

5. The method of claim 3 further comprising:

generating an exercise score for the designated exercise based on both the motion score and the heart rate score.

6. The method of claim 5 further comprising:

generating a set score by summing the individual exercise scores for a set of repetitions of the designated exercise performed by the user.

7. The method of claim 1 wherein at least one motion signature in the library of motion signatures corresponds to an exercise selected from the group consisting of squats, lungs, press ups, sit-ups, jumping jacks, running on the spot, side lunge, squat jumps, rotating lunge, high jumps, crunches, standups, and burpees.

8. The method of claim 1 wherein a motion signature corresponds to a single repetition of an exercise selected from the group consisting of a squat, a lunge, a press up, a sit-up, and a burpee.

9. The method of claim 1 wherein calculating a motion score is based on the magnitude of the motion performed by the user.

10. The method of claim 1 further comprising:

displaying, by the mobile device, a progress indicator selected from the group consisting of the number of repetitions of the designated exercise already performed in the set, the number of repetitions of the designated exercise remaining to be performed in the set and the time elapsed since the beginning of the set.

11. The method of claim 1 wherein the sensor device attaches to an ear of the user.

12. The method of claim 1 wherein the time series of sensor data further comprises data from at least one sensor selected from the group consisting of an accelerometer, a magnetometer and a gyroscope and the at least one sensor is also included in the sensor device.

13. A mobile device, comprising:

a processor;

a wireless transceiver in communication with a sensor device, the sensor device attached to the head or torso of a user; and

a non-transitory memory in communication with the processor for storing (1) a library of motion signatures, wherein a motion signature for an exercise comprises a sequence of characteristic features and wherein a characteristic feature is a movement in the exercise, and (2) instructions, which when executed by the processor, cause the mobile device;

to receive a time series of data from the sensor device, said data comprising accelerometer data from an accelerometer included in the sensor device;

to detect a single repetition of a designated exercise performed by the user, wherein detecting comprises;

segmenting the accelerometer data into a sequence of characteristic features as specified by the motion signature that corresponds to the designated exercise; and

to calculate a motion score for the single repetition of the detected exercise.

14. The device of claim 13 wherein detecting further comprises validating that the range of values in each segment of accelerometer data is within a specified range of values.

15. The device of claim 13 where the received time series of data further comprises data from a photoplethysmograph included in the sensor device, wherein the instructions, when executed by the processor, further cause the mobile device:

to calculate a heart rate for the user; and

to calculate a heart rate score for the designated exercise.

16. The device of claim 15 wherein the heart rate score is based on the increase in the user'"'"'s heart rate while performing the designated exercise.

17. The device of claim 16 wherein the instructions, when executed by the processor, further cause the mobile device:

to generate an exercise score for the designated exercise based on both the motion score and the heart rate score.

18. The device of claim 17 wherein the instructions, when executed by the processor, further cause the mobile device:

to generate a set score by summing the exercise scores for each repetition of a set of repetitions of the designated exercise performed by the user.

19. The device of claim 13 wherein at least one motion signature in the library of motion signatures corresponds to an exercise selected from the group consisting of squats, lunges, press ups, sit-ups, jumping jacks, running on the spot, side lunge, squat jumps, rotating lunge, high jumps, crunches, standups, and burpees.

20. The device of claim 13 wherein a motion signature corresponds to a single repetition of a discrete exercise selected from the group consisting of a squat, a lunge, a press up, a sit-up, and a burpee.

21. The device of claim 22 wherein calculating a motion score is based on the magnitude of the motion performed by the user.

22. The device of claim 13 wherein the instructions, when executed by the processor, further cause the mobile device:

to display, by the mobile device, a progress indicator selected from the group consisting of the number of repetitions of the designated exercise already performed in the set, the number of repetitions of the designated exercise remaining to be performed in the set and the time elapsed since the beginning of the set.

23. The device of claim 13 wherein the sensor device worn by the user is attached to the head or torso of the user.

24. The device of claim 13 wherein the time series of sensor data further comprises data from at least one sensor selected from the group consisting of an accelerometer, a magnetometer and a gyroscope and the at least one sensor is also included in the sensor device.

25. A system for detecting and scoring exercises, comprising:

a sensor device that attaches to the head of a user comprising;

an accelerometer that generates a time series of tri-axial data that measures the acceleration of the user'"'"'s head or torso; and

a wireless transmitter that transmits the time series of tri-axial accelerometer data;

a mobile device, comprising;

a processor;

a wireless transceiver in communication with the sensor device that receives the time series of tri-axial accelerometer data from the sensor device; and

a non-transitory memory in communication with the processor for storing (1) the time series of tri-axial accelerometer data, (2) a library of motion signatures, wherein a motion signature for an exercise comprises a sequence of characteristic features and wherein a characteristic feature is a movement in the exercise, and (3) instructions, which when executed by the processor, cause the mobile device;

to detect a single repetition of a designated exercise performed by the user, wherein detecting comprises;

segmenting the accelerometer data into a sequence of characteristic features as specified by the motion signature that corresponds to the designated exercise; and

to calculate a motion score for the single repetition of the detected exercise.