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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yao Rong Moncel, N. Yan Zhang Dongye Zhang Dongmei Hao |
| Copyright Year | 2011 |
| Description | Author affiliation: College of Life Science and Bioengineering, Beijing, University of Technology, China (Yao Rong; Moncel, N.; Yan Zhang; Dongye Zhang; Dongmei Hao) |
| Abstract | In order to detect muscle fatigue effectively, we recorded surface electromyographic (sEMG) signals on the right upper limbs of ten young men while they were implementing handgrip tasks. Wavelet packet transform and back propagation neural network were designed to extract features of sEMG and recognize the muscle states. 7-fold cross-validation was used to test the results. Our results showed a very efficient fatigue recognition using these methods even if a larger scale analysis would have been better. The study indicates that muscle fatigue could be detected by analyzing the sEMG signals, which allow us to consider a promising future for practical applications. |
| Starting Page | 2478 |
| Ending Page | 2480 |
| File Size | 262366 |
| Page Count | 3 |
| File Format | |
| ISBN | 9781424493043 |
| e-ISBN | 9781424493067 |
| DOI | 10.1109/CISP.2011.6100699 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-10-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | back propagation neural network Surface waves wavelet packet transform Muscles Fatigue Electromyography Wavelet packets classification muscle fatigue surface electromyography signals |
| Content Type | Text |
| Resource Type | Article |
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