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A Wireless sEMG Recording System and Its Application to Muscle Fatigue Detection

Department of Photonics and Communication Engineering, Asia University, Taichung 41349, Taiwan
Graduate Institute of Clinical Medical Science, China Medical University, Taichung 41349, Taiwan
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
Author to whom correspondence should be addressed.
Sensors 2012, 12(1), 489-499;
Received: 6 December 2011 / Revised: 30 December 2011 / Accepted: 3 January 2012 / Published: 5 January 2012
(This article belongs to the Section Physical Sensors)
Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric muscle contraction are clearly represented in the receiving user interface. Muscle fatigue detection is an important application of sEMG. 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. A more negative slope value represents a higher muscle fatigue condition. To test the system performance, muscle fatigue detection was examined by having subjects run on a pedaled-multifunctional elliptical trainer for approximately 30 minutes at three loading levels. Ten subjects underwent a total of 60 exercise sessions to provide the experimental data. Results showed that the regression slope gradually decreases as expected, and there is a significant gender difference. View Full-Text
Keywords: sEMG; muscle fatigue; Bluetooth; regression slope sEMG; muscle fatigue; Bluetooth; regression slope
MDPI and ACS Style

Chang, K.-M.; Liu, S.-H.; Wu, X.-H. A Wireless sEMG Recording System and Its Application to Muscle Fatigue Detection. Sensors 2012, 12, 489-499.

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