Metrics and algorithms for interpretation of muscular use

• US 9,131,888 B2
• Filed: 09/21/2011
• Issued: 09/15/2015
• Est. Priority Date: 09/21/2010
• Status: Expired due to Fees

• Current Assignee: Somaxis Incorporated
• Original Assignee: Somaxis Incorporated
• Inventors: Grey, Alexander B., Georgiou, Violetta, Hejtmanek, Edward Frank
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): SORIANO, BOBBY GILES
• Application Number:
  US13/239,064
• Publication Number:
  US 20120071732A1
• Time in Patent Office: 1,455 Days
• Field of Search: 600/300, 600/301, 600/372, 600/546, 600/595
• 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:

physically coupling;

at least one surface electromyometry (sEMG) sensor to a subject;

at least one temperature sensor to the subject; and

at least one heart rate sensor to the subject;

the at least one sEMG sensor, at least one temperature sensor, and at least one heart rate sensor also being communicably coupled to a computing system;

the computing system gathering data from the at least one sEMG sensor, at least one temperature sensor, and at least one heart rate sensor physically coupled to the subject while the subject is engaged in a muscle activity;

the computing system monitoring and/or recording relevant data of the subject engaged in the muscle activity from the at least one sEMG sensor, at least one temperature sensor, and at least one heart rate sensor physically coupled to the subject;

determining at least one muscle fatigue onset index (MFOI), the determining of the at least one MFOI comprising;

selecting a MFOI protocol;

the computing system plotting sEMG mean power frequency (MPF) amplitude data points gathered over a period of time to a line;

the computing system determining the slope of the line as the rate of muscle fatigue; and

the computing system determining the at least one MFOI from the slope of the line as the rate of muscle fatigue from the plot of the sEMG MPF amplitude data points gathered over the period of time to the line; and

the computing system using the MFOI to estimate how quickly the subject'"'"'s muscles will become fatigued.

2. The method of claim 1, further comprising:

the computing system plotting sEMG amplitude data points gathered at set time intervals.

3. The method of claim 1, further comprising:

the computing system plotting maximum sEMG amplitude data points gathered continuously.

4. The method of claim 1, further comprising:

the computing system plotting average sEMG amplitude data for an epoch gathered continuously.

5. The method of claim 1, comprising determining chronic muscle overuse index (CMOI), the CMOI determining comprising:

selecting CMOI protocol;

performing a calibration activity;

plotting sEMG amplitude data points gathered over a period of time to a curve to establish a dataset;

referencing a database to determine a related average dataset;

determining CMOI based on the relationship between the dataset and the related average dataset; and

determining a conversion factor based on the ratio of the rate of change for non-ideal conditions to the rate of change for ideal conditions.

6. The method of claim 5, further comprising projecting estimated chronic muscle overuse, the projecting estimated chronic muscle overuse comprising:

applying a conversion factor to project estimated amplitude for muscle exhaustion for a given activity; and

determining a time associated with the estimated amplitude for muscle exhaustion.

7. The method of claim 5, further comprising determining a warm up index (WUI), the determining the WUI comprising:

recording initial sEMG amplitude as an initial WUI;

recording the corresponding frequency;

referencing a database to determine a related average dataset;

determining a second WUI based on the related average dataset; and

determining a third WUI based on the user input of a related average dataset.

8. The method of claim 1, further comprising determining chronic muscle overuse index (CMOI), the determining CMOI comprising;

selecting CMOI protocol;

performing a calibration activity;

plotting sEMG frequency data points gathered over a period of time to a line;

referencing a database to determine a related average dataset;

determining CMOI based on the relationship of the subject data and the related average dataset; and

determining a conversion factor based on the ratio of the rate of change for non-ideal conditions to the rate of change for ideal conditions.

9. The method of claim 8, further comprising projecting estimated chronic muscle overuse, the projecting estimated chronic muscle overuse comprising:

applying a conversion factor to project an estimated frequency for muscle exhaustion for a given activity; and

determining a time associated with the estimated frequency for muscle exhaustion.

10. The method of claim 8, further comprising determining safety zones, the determining safety zones comprising:

calculating the amplitude/MPF ranges for each zone; and

calculating the time associated with the boundaries for each zone.

11. The method of claim 1, further comprising determining an impulsive muscle overuse index (IMOI), the IMOI determining comprising:

selecting IMOI protocol;

performing a calibration activity in a semi-controlled environment;

recording pseudo maximum voluntary contractions (PMVC) data;

associating PMVC data with known maximum voluntary contraction (MVC) data in a controlled environment into a combined database;

referencing the combined database to determine a relate average dataset;

determining IMOI based on an safe threshold amplitude of the related average dataset; and

determining a conversion factor based on the ratio of the MVC data to the PMVC data.

12. The method of claim 11, further comprising projecting estimated impulsive muscle overuse, the projecting estimated impulsive muscle overuse comprising:

applying a conversion factor to project a safe threshold estimated amplitude for a given activity; and

determining a time associated with the safe threshold estimated amplitude for muscle exhaustion.

13. The method of claim 11, further comprising determining a weighted IMOI, the determining of the weighted IMOI comprising:

determining a safety constant as some number less than 1 and more than zero; and

multiplying IMOI by the safety constant to determine a weighted IMOI.

14. The method of claim 1, further comprising determining muscular work estimation index (MWEI), the determining of the muscular work estimation indes comprising:

selecting MWEI protocol;

performing a calibration activity in a semi-controlled environment;

recording sEMG amplitude data and work data for associated activity;

plotting data points on respective graphs;

finding the integrated sEMG amplitude value and the sum of the work output value;

determining a conversion factor based on the ratio of work output value to the integrated sEMG value;

storing the data and associated conversion factor in a database;

referencing the combined database for a related average dataset conversion factor to new user sEMG data; and

determining MWEI by multiplying a new user sEMG data by the related average dataset conversion factor.

15. The method of claim 1, further comprising determining skeletal muscle calorie index (SMCI), the determining SMCI comprising:

selecting SMCI protocol;

performing a calibration;

recording both sEMG amplitude data and caloric expenditure data for associated activity;

plotting data points on respective graphs;

determining the integrated sEMG amplitude value and the total caloric expenditure value;

determining a conversion factor based on the ratio of the total caloric expenditure value to the integrated sEMG amplitude value;

referencing a database to determine a related average dataset;

determining an average conversion factor based on the ratio of a related sum dataset to the total number of datasets sampled; and

determining SMCI by multiplying a the average conversion factor by the sum integrated sEMG data associated with the related sum dataset.

16. The method of claim 15, further comprising:

a conversion factor for a single muscle; and

a conversion factor for at least one muscle or muscle group.

17. The method of claim 1, further comprising determining cardiovascular muscle calorie index (CMCI), the determining CMCI comprising:

selecting CMCI protocol;

performing a calibration;

recording both sEMG amplitude data and heart rate (HR) data for associated activity;

plotting data for sEMG data;

referencing HR data with a known database to determine a first estimate of calories burned (CHR);

referencing the integrated sEMG data with SMCI to determine a second estimate of calories burned (CRS); and

determining CMCI by adding CHR to CRS and dividing by 2.

18. The method of claim 17, further comprising determining a weighted CMCI, the determining the weighted CMCI comprising:

multiplying CHR by a first weighted constant between zero and one to establish a CHR weighted value;

multiplying CRS by a second weighted constant between zero and one to establish a CRS weighted value;

wherein the sum of the first and second weighted constant equals 1; and

determining the weighted CMCI by adding the CHR weighted value to the CRS weighted value.

19. The method of claim 1, further comprising determining muscle activation symmetry index (MASI) and muscle condition, the determining MASI comprising:

selecting MASI protocol;

performing a calibration;

recording rectified sEMG amplitude data for a right side of a symmetrical muscle or muscle group;

recording rectified amplitude data for a left side of a symmetrical muscle or muscle group;

determining MASI by subtracting rectified right-side amplitude from rectified left-side amplitude;

referencing a database to determine an average related dataset known as normalized muscle activation symmetry index (NMASI); and

determining the condition of a symmetrical muscle group by comparing MASI to NMASI.

20. The method of claim 19, further comprising determining muscle condition in symmetrical muscle groups, the determining muscle condition in symmetrical muscle groups comprising:

designating muscle condition as normal if MASI<

NMASI; and

designating a muscle condition as abnormal if MASI>

NMASI.

21. The method of claim 19, further comprising determining MASI, the determining MASI comprising:

measuring MASI over time for the same individual and muscle or muscle group; and

averaging MASI over time by calculating the sum and dividing by the number of samples.

22. The method of claim 14, further comprising determining muscle work difference index (MWDI), the determining MWDI comprising:

performing a MWEI protocol for a plurality of muscles or muscle groups;

determining at least a first value and a second value representing the work done for each muscle or muscle group;

subtracting the at least first value from the at least second value to determine MWDI;

referencing a database to determine an average related dataset known as normalized muscle work difference index (NMWDI); and

determining the condition of a muscle or muscle group by comparing a MWDI to NMWDI.

23. The method of claim 22, further comprising determining muscle condition, the determining muscle condition comprising:

designating muscle condition as normal if MWDI≦

NMWDI; and

designating a muscle condition as abnormal if MWDI>

NMWDI.

24. The method of claim 1, further comprising determining slow-twitch muscle indexes (STMI), the determining STMI comprising:

selecting STMI protocol;

performing a calibration activity;

recording sEMG amplitude data for the subject engaged in the activity;

plotting data gathered on an amplitude versus time graph;

noting the inflection point;

determining a corresponding inflection point on an MPF versus time graph;

filtering out all data coming before the inflection point to determine activity dominated by slow-twitch muscle fibers; and

calculate one or more of the maximum amplitude of slow-twitch fiber activity, average amplitude of slow-twitch muscle fibers, the rate of fatigue of slow-twitch fibers, the length of time required before slow-twitch fibers become the primary force behind the activity; and

/or

work done by slow-twitch fibers during the activity.

25. The method of claim 1, further comprising determining fast-twitch muscle indexes (FTMI), the determining FTMI comprising:

selecting FTMI protocol;

performing a calibration activity;

recording sEMG amplitude data for a subject engaged in the activity;

plotting data gathered on an amplitude versus time graph;

noting the inflection point;

determining a corresponding inflection point on an MPF versus time graph;

filtering out all data coming after the inflection point to determine activity dominated by slow-twitch muscle fibers; and

calculate one or more of the maximum amplitude of fast-twitch fiber activity, average amplitude of fast-twitch muscle fibers, the rate of fatigue of fast-twitch fibers, the length of time fast-twitch fibers are the primary force behind the activity; and

/or

work done by fast-twitch fibers during the activity.

26. The method of claim 25, further comprising determining caloric expenditure, the determining caloric expenditure comprising:

determining caloric expenditure of slow-twitch fiber activity by converting the integral sEMG data to caloric expenditure with SMCI protocol.

27. The method of claim 1, further comprising determining personal multidimensional fitness index (PMFI), the determining PMFI comprising:

selecting PMFI protocol;

gathering a plurality of index values;

determining an actual first value from a subject'"'"'s activity related to the probability of the individual to experience muscle fatigue;

determining an actual second value from a subject'"'"'s activity related to the proximity of that individual'"'"'s heart rate to match a known target heart rate;

determining an actual third value from a subject'"'"'s activity related to the proximity of an individual'"'"'s body-mass index (BMI) to match a known target heart rate;

determining an actual fourth value from a subject'"'"'s activity related to the proximity of an individual'"'"'s maximal oxygen intake (VO2_max) data point to match a known target VO2_max data point;

summing at least two of the absolute actual values of the first, second, third, and fourth values to create a resulting actual value; and

subtracting the actual value from an ideal value resulting in a multidimensional fitness metric.

28. A muscle assessment method comprising:

physically coupling;

at least one surface electromyometry (sEMG) sensors to a subject;

at least one temperature sensor to the subject; and

at least one heart rate sensors to the subject;

the at least one sEMG sensor, at least one temperature sensor, and at least one heart rat sensor also being communicably coupled to a computing system;

the computing system gathering data from the at least one sEMG sensor, at least one temperature sensor, and at least one heart rate sensor while the subject is engaged in a muscle activity;

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

determining at least one muscle fatigue onset index (MFOI), the determining of the MFOI comprising;

selecting a MFOI protocol;

the computing system plotting sEMG mean power frequency (MPF) amplitude data points gathered over a period of time to a line;

the computing system determining the slope of the line as the rate of muscle fatigue;

the computing system determining the at least one MFOI from the slope of the line as the rate of muscle fatigue from the plot of the sEMG MPF amplitude data points gathered over the period of time to the line; and

the computing system comparing the MFOI to a set of MFOI from other subjects to estimate how physically fit the subject is.

29. The method of claim 28, further comprising:

determining variables for use in determining the MFOI including;

a frequency sampling rate (FSR); and

time of activity (AT).

30. The method of claim 29, further comprising the computing system calculating a quantity of total samples (QS) for calculating the MFOI as defined by the following equation:

QS−

(AT/FSR)+1.