Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation
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Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, Tianjin University, Tianjin 300354, China
2
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
3
Department of Intelligent Mechanical Systems Engineering, Kagawa University, Kagawa Prefecture 761-0396, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2016, 16(10), 1739; https://doi.org/10.3390/s16101739
Received: 2 June 2016
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Revised: 20 August 2016
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Accepted: 21 September 2016
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Published: 19 October 2016
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.
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Keywords:
sEMG; continuous recognition; rehabilitation robotics
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MDPI and ACS Style
Song, Z.; Zhang, S. Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation. Sensors 2016, 16, 1739. https://doi.org/10.3390/s16101739
AMA Style
Song Z, Zhang S. Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation. Sensors. 2016; 16(10):1739. https://doi.org/10.3390/s16101739
Chicago/Turabian StyleSong, Zhibin, and Songyuan Zhang. 2016. "Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation" Sensors 16, no. 10: 1739. https://doi.org/10.3390/s16101739
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