Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthet...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/7/3374 |
| _version_ | 1827624455335051264 |
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| author | Shengli Zhou Fei Fei Kuiying Yin |
| author_facet | Shengli Zhou Fei Fei Kuiying Yin |
| author_sort | Shengli Zhou |
| collection | DOAJ |
| description | Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthetic control, sometimes it may cause a disaster. A two-layer classifier that fuses the Gaussian mixture model (GMM) and k-nearest neighbor (kNN) in a sequential structure is proposed in this study. The proposed algorithm can reject the trained movements with low reliability and is efficient in rejecting the untrained movements, thus enhancing the reliability of the myoelectric control system. The results show that the proposed algorithm can produce 95.7% active accuracy in recognizing 12 trained movements and a 30.3% error rate for rejecting 12 untrained movements. When the movement number is six, the active accuracy for trained movements can reach 99.2%, and the error rate of untrained movement is only 17.4%, which is much better than previous studies. Therefore, the proposed classifier can accurately recognize the trained movements and reject untrained movement patterns effectively. |
| first_indexed | 2024-03-09T12:08:52Z |
| format | Article |
| id | doaj.art-9fec87aff5b74bd491f13843431dc23c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:08:52Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9fec87aff5b74bd491f13843431dc23c2023-11-30T22:54:52ZengMDPI AGApplied Sciences2076-34172022-03-01127337410.3390/app12073374Toward Improving the Reliability of Discrete Movement Recognition of sEMG SignalsShengli Zhou0Fei Fei1Kuiying Yin2School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, ChinaNanjing Research Institute of Electronic Technology, Nanjing 211100, ChinaCurrently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthetic control, sometimes it may cause a disaster. A two-layer classifier that fuses the Gaussian mixture model (GMM) and k-nearest neighbor (kNN) in a sequential structure is proposed in this study. The proposed algorithm can reject the trained movements with low reliability and is efficient in rejecting the untrained movements, thus enhancing the reliability of the myoelectric control system. The results show that the proposed algorithm can produce 95.7% active accuracy in recognizing 12 trained movements and a 30.3% error rate for rejecting 12 untrained movements. When the movement number is six, the active accuracy for trained movements can reach 99.2%, and the error rate of untrained movement is only 17.4%, which is much better than previous studies. Therefore, the proposed classifier can accurately recognize the trained movements and reject untrained movement patterns effectively.https://www.mdpi.com/2076-3417/12/7/3374myoelectric signalpattern recognitionmachine learningsEMGreject optionGMM |
| spellingShingle | Shengli Zhou Fei Fei Kuiying Yin Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals Applied Sciences myoelectric signal pattern recognition machine learning sEMG reject option GMM |
| title | Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals |
| title_full | Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals |
| title_fullStr | Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals |
| title_full_unstemmed | Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals |
| title_short | Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals |
| title_sort | toward improving the reliability of discrete movement recognition of semg signals |
| topic | myoelectric signal pattern recognition machine learning sEMG reject option GMM |
| url | https://www.mdpi.com/2076-3417/12/7/3374 |
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