Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Journal: Computer methods and programs in biomedicine

Published Date: Nov 9, 2017

Abstract

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.

Authors

P A Karthick

Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India; Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. Electronic address: pakarthick1@gmail.com.
Diptasree Maitra Ghosh

Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
S Ramakrishnan

Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India.

Keywords

Algorithms Arm Automation Electromyography Humans Machine Learning Models, Theoretical Muscle Contraction Muscle Fatigue Muscle, Skeletal Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (29249346)

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Abstract

Authors

Keywords

External Resources

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