TY - JOUR
T1 - A subject-independent method for automatically grading electromyographic features during a fatiguing contraction
AU - Chattopadhyay, Rita
AU - Jesunathadas, Mark
AU - Poston, Brach
AU - Santello, Marco
AU - Ye, Jieping
AU - Panchanathan, Sethuraman
N1 - Funding Information:
Manuscript received November 1, 2011; revised February 29, 2012; accepted March 15, 2012. Date of publication April 6, 2012; date of current version May 18, 2012. This work was supported by the National Institute of Arthritis, Musculoskeletal and Skin Diseases at the National Institutes of Health, under Grant 2R01 AR47301. Asterisk indicates corresponding author.
PY - 2012
Y1 - 2012
N2 - Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.
AB - Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.
KW - Computer algorithm
KW - electromyogram (EMG)
UR - http://www.scopus.com/inward/record.url?scp=84861355523&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861355523&partnerID=8YFLogxK
U2 - 10.1109/TBME.2012.2193881
DO - 10.1109/TBME.2012.2193881
M3 - Article
C2 - 22498666
AN - SCOPUS:84861355523
SN - 0018-9294
VL - 59
SP - 1749
EP - 1757
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 6
M1 - 2193881
ER -