METRICS & ALGORITHMS FOR INTERPRETATION OF MUSCULAR USE INCLUDING CHRONIC MUSCLE OVERUSE INDEX

• US 20150374302A1
• Filed: 09/08/2015
• Published: 12/31/2015
• Est. Priority Date: 09/21/2010
• Status: Abandoned Application

• Current Assignee: Somaxis Incorporated
• Original Assignee: Somaxis Incorporated
• Inventors: Grey, Alexander B., Georgiou, Violetta, Hejtmanek, Edward Frank
• Application Number:
  US14/847,714
• Publication Number:
  US 20150374302A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0238 Means for recording calibration data
• CPC Class Codes: A61B 2560/0412 Low-profile patch shaped housings
• CPC Class Codes: A61B 2562/164 the sensor is mounted in or on a conformable substrate or carrier
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• 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 5/112 Gait analysis
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• CPC Class Codes: A61B 5/224 Measuring muscular strength
• CPC Class Codes: A61B 5/25 Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/296 for electromyography [EMG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6833 Adhesive patches
• 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 5/725 : using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• 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: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G16H 20/10 : relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

A muscle assessment method utilizing a computing system, surface electromyometry (sEMG) sensors, and other sensors to gather data for one or more subjects engaged in an activity through operably coupling the one or more sensors to the computing system, and directing a computing system to select one or more muscle assessment protocols related to a number of different metrics. For a user subject engaged in a physical activity, assessing muscle condition, the muscle activity, and statistically related averages provide information to the user and about muscle and whole body fitness.

Claims

1. A muscle assessment method comprising:

determining a chronic muscle overuse index (CMOI), comprising;

selecting a CMOI protocol including an initial calibration activity;

creating a subject data set, including;

monitoring and/or recording relevant demographic data of the subject engaged in the muscle activity;

gathering surface electromyometry (sEMG) data from one or more sEMG sensors attached to the subject during the initial calibration activity;

plotting sEMG amplitude data points gathered over a period of time to establish the subject'"'"'s data set;

referencing a database of secondary stored demographic and sEMG data sets to determine a related average dataset, the related average dataset being a subset of the secondary stored data sets based on a comparison of the subject'"'"'s data set to the database of secondary stored demographic and sEMG data sets;

determining the CMOI based on a relationship between the subject'"'"'s data set and the related average dataset.

2. The method of claim 1, further comprising projecting an estimated point of chronic muscle overuse beyond the initial calibration activity using the CMOI.

3. The method of claim 2, wherein the projected estimated point of chronic muscle overuse considers a projected sEMG data amplitude attribute of the subject beyond the initial calibration activity.

4. The method of claim 3, wherein the projected sEMG data amplitude attribute of the subject includes a projected high amplitude sEMG data point, projected low amplitude sEMG data point, and/or a projected difference of sEMG amplitude between a high amplitude data point and a low amplitude data point of the subject beyond the initial calibration activity.

5. The method of claim 3, wherein the projected sEMG data amplitude attribute of the subject includes a mean power frequency of projected sEMG data of the subject beyond the initial calibration activity.

6. The method of claim 1, further comprising estimating a time to exhaustion of the subject based on the CMOI following the initial calibration activity.

7. The method of claim 1, wherein the determined CMOI considers a comparison of high amplitude sEMG data and low amplitude sEMG data in the subject data set to high amplitude sEMG data and low amplitude sEMG data in the related average data set.

8. The method of claim 1, wherein the determined CMOI considers a comparison of a difference between high amplitude sEMG data and low amplitude sEMG data in the subject data set to a difference in high amplitude sEMG data and low amplitude sEMG data in the related average data set.

9. The method of claim 1, wherein the related average data set is identified by a comparison of high amplitude sEMG data and low amplitude sEMG data in the subject data set to high amplitude sEMG data and low amplitude sEMG data in the secondary data sets.

10. The method of claim 1, wherein the related average data set includes a subset of at least 35 data sets of the secondary data sets.

11. The method of claim 1, wherein the relevant demographic data of the subject engaged in the muscle activity includes an age, height, and/or weight of the subject.

12. The method of claim 11, wherein the relevant demographic data of the subject engaged in the muscle activity further includes a fitness attribute of the subject.

13. The method of claim 1, wherein the subject data set further includes one or more activity attributes, wherein the one or more activity attributes includes an activity type, an activity level, or rate of exhaustion associated with the activity.

14. The method of claim 1, further comprising continuing to monitor the subject'"'"'s continued activity after the initial calibration activity by gathering sEMG data from the one or more sEMG sensors attached to the subject during the continued activity.

15. The method of claim 14, further comprising monitoring an observed rate of change (ROC) of amplitude in the sEMG data during the continued activity.

16. The method of claim 15, further comprising calculating a conversion factor based on the ROC and applying this conversion factor to identify a new estimated point of chronic muscle overuse.

17. The method of claim 16, wherein the new projected estimated chronic muscle overuse considers a new projected sEMG data amplitude attribute of the subject at the new projected estimated point of chronic muscle overuse.

18. The method of claim 15, wherein the new projected sEMG data amplitude attribute of the subject includes a new projected high amplitude sEMG data, new projected low amplitude sEMG data, and/or new a projected difference of sEMG amplitude between a high amplitude data and a low amplitude data.

19. The method of claim 15, wherein the new projected sEMG data amplitude attribute of the subject includes a new mean power frequency of projected sEMG data.

20. The method of claim 15, further comprising estimating a new time to exhaustion of the subject based on the conversion factor.

21. A muscle assessment method comprising:

determining a chronic muscle overuse index (CMOI), comprising;

selecting a CMOI protocol including an initial calibration activity;

creating a subject data set, including;

monitoring and/or recording relevant demographic data of the subject engaged in the muscle activity;

gathering surface electromyometry (sEMG) data from one or more sEMG sensors attached to the subject during the initial calibration activity;

plotting sEMG amplitude data points gathered over a period of time to establish the subject'"'"'s data set;

referencing a database of secondary stored demographic and sEMG data sets to determine a related average dataset, the related average dataset being a subset of the secondary stored data sets based on a comparison of the subject'"'"'s data set to the database of secondary stored demographic and sEMG data sets;

determining the CMOI based on a relationship between the subject'"'"'s data set and the related average dataset; and

estimating a time to exhaustion of the subject based on the CMOI following the initial calibration activity;

dividing the estimated time to exhaustion of the subject into two or more zones; and

alerting the subject as to the one or more zones based on an elapsed time and/or additional sEMG amplitude continued activity data gathered subsequent to the initial calibration activity;

monitoring an observed rate of change (ROC) of amplitude in the sEMG data during the continued activity; and

modifying the two or more zones of the estimated time to exhaustion of the subject based on the observed ROC.