| CAI Huihui,ZHANG Yakun,XIE Liang,YIN Erwei,YAN Ye,MING Dong.Electromyography signal segmentation method based on spectral subtraction backtracking[J].Optoelectronics Letters,2022,(10):623-627 |
| Electromyography signal segmentation method based on spectral subtraction backtracking |
| Author Name | Affiliation | | CAI Huihui | Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
Tianjin Artificial Intelligence Innovation Center TAIIC, Tianjin 300450, China
Defense Innovation Institute, Academy of Military Sciences AMS, Beijing 100071, China | | ZHANG Yakun | Tianjin Artificial Intelligence Innovation Center TAIIC, Tianjin 300450, China
Defense Innovation Institute, Academy of Military Sciences AMS, Beijing 100071, China | | XIE Liang | Tianjin Artificial Intelligence Innovation Center TAIIC, Tianjin 300450, China
Defense Innovation Institute, Academy of Military Sciences AMS, Beijing 100071, China | | YIN Erwei | Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
Tianjin Artificial Intelligence Innovation Center TAIIC, Tianjin 300450, China
Defense Innovation Institute, Academy of Military Sciences AMS, Beijing 100071, China | | YAN Ye | Tianjin Artificial Intelligence Innovation Center TAIIC, Tianjin 300450, China
Defense Innovation Institute, Academy of Military Sciences AMS, Beijing 100071, China | | MING Dong | Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China |
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| Abstract: |
| Surface electromyography (EMG) is a bioelectrical signal that recognizes speech contents in a non-acoustic form. Activity detection is an important research direction in EMG research. However, in the low signal-to-noise ratio (SNR) environment, it is difficult for traditional methods to obtain accurate active signals. This paper proposes a new energy-based spectral subtraction backtracking (E-SSB) method to segment EMG active signal in the low SNR environment. Compared with traditional energy detection, the algorithm in this paper adds spectral subtraction (SS) to filter out the clutter, and raises a retrospective idea to improve the classification performance. The experiment results show the proposed activity detection method is more effective than other methods in the low SNR environment. |
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