NDLI: Exercise muscle fatigue detection system implementation via wireless surface electromyography and empirical mode decomposition

Content Provider	IEEE Xplore Digital Library
Author	Kang-Ming Chang Shing-Hong Liu Jia-Jung Wang Da-Chuan Cheng
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Chaoyang Univ. of Technol., Taichung, Taiwan (Shing-Hong Liu) \|\| Dept. of Biomed. Imaging & Radiol. Sci., China Med. Univ., Taichung, Taiwan (Da-Chuan Cheng) \|\| Dept. of Biomed. Eng., I-Shou Univ., Kaoshiung, Taiwan (Jia-Jung Wang) \|\| Asia Univ., Taichung, Taiwan (Kang-Ming Chang)
Abstract	Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. A wireless Bluetooth transmission sEMG measurement system with a sampling frequency of 2 KHz is developed. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. As fatigue increases, the power spectrum of the sEMG shifts toward lower frequencies. The goal of this study is to evaluate the sensitivity of empirical mode decomposition (EMD) quantifying the electrical manifestations of the local muscle fatigue during exercising in health people. We also compared this method with the raw data and discrete wavelet transform (DWT). Five male and five female volunteers participated. Each subject was asked to run on a multifunctional pedaled elliptical trainer for about 30 minutes, twice a week, and there were a total of six recording times for each subject with a wireless EMG recording system. The results show that sensitivity of the highest frequency component of EMD is better than the highest frequency component of DWT, and raw data.
Starting Page	1001
Ending Page	1004
File Size	473660
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609672
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Fatigue Discrete wavelet transforms Empirical mode decomposition Electromyography Wireless communication Electrodes
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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