Metrics & algorithms for interpretation of muscular use including chronic muscle overuse index

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.

What is claimed is: 
     
         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.