SYSTEMS, METHODS AND DEVICES FOR EXERCISE AND ACTIVITY METRIC COMPUTATION

• US 20150272483A1
• Filed: 03/26/2015
• Published: 10/01/2015
• Est. Priority Date: 03/26/2014
• Status: Active Grant

• Current Assignee: Trenda Innovations, Inc.
• Original Assignee: GestureLogic Inc.
• Inventors: ETEMAD, S. Ali, HOUMANFAR, Roshanak, KLIBANOV, Mark, MACEACHERN, Leonard, FIROUZI, Kaveh
• Granted Patent: US 10,327,670 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• 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 5/22 Ergometry; Measuring muscular strength or the force of a muscular blow exercising apparatus A63B21/00 ; measuring of work or force in general G01L
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: H04B 1/385 Transceivers carried on the body, e.g. in helmets
• CPC Class Codes: H04B 1/3888 Arrangements for carrying or protecting transceivers
• CPC Class Codes: H04B 2001/3855 : carried in a belt or harness

Abstract

Systems, methods and devices that facilitate determination of enhanced exercise or physical activity metrics by considering multiple types of data. Metrics are computable by pre-processing, and in some cases segmenting, a variety of input signals, such as acceleration signals, electromyography signals or other signals from a wearable device.

Claims

1. A method of determining exercise metrics for a user'"'"'s body using a wearable device, the method comprising:

receiving a plurality of signals from at least one sensor of the wearable device;

pre-processing the plurality of signals to generate a plurality of pre-processed signals;

using a processor, discriminating the plurality of pre-processed signals to generate a plurality of signal segments for each of the plurality of pre-processed signals;

computing at least one exercise metric based on the plurality of signal segments of the plurality of pre-processed signals.

2. The method of claim 1, wherein the at least one sensor comprises an accelerometer, and the plurality of signals comprise acceleration signals.

3. The method of claim 1, wherein the at least one sensor comprises a plurality of EMG sensors and at least two of the EMG sensors are positioned to detect EMG signals from different muscles of the user.

4. The method of claim 3, wherein the at least one exercise metric comprises a muscle intensity metric.

5. The method of claim 3, wherein the at least one exercise metric comprises a muscle coordination metric.

6. The method of claim 3, wherein the at least one exercise metric comprises a muscle ratio metric.

7. The method of claim 3, wherein the at least one exercise metric comprises a muscle fatigue metric, and wherein the computing comprises computing a shift in a frequency characteristic of at least one EMG signal.

8. The method of claim 3, wherein generating the plurality of signal segments comprises determining a temporal distance between the plurality of signal segments.

9. The method of claim 1, wherein the at least one sensor comprises a sensor selected from the group consisting of a gyroscope and a magnetometer.

10. The method of claim 2, wherein filtering the plurality of signals comprises low pass filtering each of the plurality of signals.

11. The method of claim 2, wherein generating the plurality of signal segments comprises generating a plurality of feature vectors based on the plurality of pre-processed signals, wherein each feature vector comprises a plurality of feature values, and wherein each feature vector is associated with a time step of the pre-processed signals.

12. The method of claim 11, wherein each feature vector comprises time domain and frequency domain data based on the plurality of pre-processed signals.

13. The method of claim 11, wherein each feature vector is computed over a moving time window.

14. The method of claim 11, wherein generating the plurality of signal segments further comprises mapping the plurality of feature vectors onto a predetermined feature space.

15. The method of claim 14, wherein the mapping is predetermined by ANOVA computation over a training data set.

16. The method of claim 14, wherein the predetermined feature space is predetermined by Principal Component Analysis of a training data set.

17. The method of claim 11, wherein computing at least one exercise metric comprises performing, using the processor, hierarchical classification to compute a likeliest class for a current time step.

18. The method of claim 2, wherein generating the plurality of signal segments comprises applying an adaptive threshold.

19. A wearable device for determining exercise metrics for a user'"'"'s body, the wearable device comprising:

at least one sensor positionable on the user'"'"'s limb;

a processor operatively coupled to the at least one sensor, the processor configured to;

receive a plurality of signals from at least one sensor of the wearable device;

pre-process the plurality of signals to generate a plurality of pre-processed signals;

discriminate the plurality of pre-processed signals to generate a plurality of signal segments for each of the plurality of pre-processed signals;

compute at least one exercise metric based on the plurality of signal segments of the plurality of pre-processed signals.

20. A non-transitory computer readable medium storing computer-executable instructions, which, when executed by a computer processor, cause the computer processor to carry out a method of analyzing activity of a user'"'"'s body using a wearable device positioned on a user'"'"'s limb, the method comprising:

receiving a plurality of signals from at least one sensor of the wearable device;

pre-processing the plurality of signals to generate a plurality of pre-processed signals;

using a processor, discriminating the plurality of pre-processed signals to generate a plurality of signal segments for each of the plurality of pre-processed signals;

computing at least one exercise metric based on the plurality of signal segments of the plurality of pre-processed signals.