Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model
To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7087 |
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| author | Zhiwei Zhou Qing Tao Na Su Jingxuan Liu Qingzheng Chen Bowen Li |
| author_facet | Zhiwei Zhou Qing Tao Na Su Jingxuan Liu Qingzheng Chen Bowen Li |
| author_sort | Zhiwei Zhou |
| collection | DOAJ |
| description | To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided. |
| format | Article |
| id | doaj-art-3d723dd48838430a814af7e3fa6ee256 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3d723dd48838430a814af7e3fa6ee2562025-08-20T02:49:56ZengMDPI AGSensors1424-82202024-11-012421708710.3390/s24217087Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion ModelZhiwei Zhou0Qing Tao1Na Su2Jingxuan Liu3Qingzheng Chen4Bowen Li5College of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaCollege of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830017, ChinaTo enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided.https://www.mdpi.com/1424-8220/24/21/7087surface electromyography signalslower limb action recognitionconvolutional neural networktransformer encoderlong short-term memory |
| spellingShingle | Zhiwei Zhou Qing Tao Na Su Jingxuan Liu Qingzheng Chen Bowen Li Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model Sensors surface electromyography signals lower limb action recognition convolutional neural network transformer encoder long short-term memory |
| title | Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model |
| title_full | Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model |
| title_fullStr | Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model |
| title_full_unstemmed | Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model |
| title_short | Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model |
| title_sort | lower limb motion recognition based on semg and cnn tl fusion model |
| topic | surface electromyography signals lower limb action recognition convolutional neural network transformer encoder long short-term memory |
| url | https://www.mdpi.com/1424-8220/24/21/7087 |
| work_keys_str_mv | AT zhiweizhou lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel AT qingtao lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel AT nasu lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel AT jingxuanliu lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel AT qingzhengchen lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel AT bowenli lowerlimbmotionrecognitionbasedonsemgandcnntlfusionmodel |