Gesture Recognition for Prosthetic Hand Control Using Surface Electromyography Time-Frequency Images and Convolutional Neural Networks (Demonstration video and paper data)
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901674279305216 |
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| author | Chen, Qingzheng |
| author_facet | Chen, Qingzheng |
| contents | <p><strong><span lang="EN-US">Abstract:</span></strong><span lang="EN-US"> Surface electromyography (sEMG) signals, as a non-invasive biosignal, are widely used in human-machine interaction and prosthetic control systems. Gesture recognition using sEMG is one of the key technologies for electromyographic prosthetic hand control systems. This study proposes a gesture recognition method based on sEMG time-frequency images and convolutional neural networks (CNN), successfully applied to real-time control of various prosthetic hand gestures. sEMG data were collected and preprocessed from 12 healthy subjects, with short-time Fourier transform (STFT) used to generate time-frequency images. Five CNN models with different depths were constructed for sEMG gesture recognition. Experimental results showed that the 4-layer deep CNN model (CNN4) performed the best in 9 gesture classification tasks, achieving an accuracy of 96.88%. Among classic CNN architectures, most models achieved classification accuracy above 90%, with DenseNet performing the best at 96.81%. Additionally, the study investigated the impact of time-frequency image resolution and STFT window function time window length on CNN performance. The results demonstrated that appropriate time-frequency image resolution and window length significantly improved CNN recognition accuracy. This method successfully improves gesture recognition accuracy and real-time performance by optimizing the input of sEMG time-frequency images and CNN network structure, contributing to the advancement of myoelectric prosthetic hand control systems.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15421098 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Gesture Recognition for Prosthetic Hand Control Using Surface Electromyography Time-Frequency Images and Convolutional Neural Networks (Demonstration video and paper data) Chen, Qingzheng <p><strong><span lang="EN-US">Abstract:</span></strong><span lang="EN-US"> Surface electromyography (sEMG) signals, as a non-invasive biosignal, are widely used in human-machine interaction and prosthetic control systems. Gesture recognition using sEMG is one of the key technologies for electromyographic prosthetic hand control systems. This study proposes a gesture recognition method based on sEMG time-frequency images and convolutional neural networks (CNN), successfully applied to real-time control of various prosthetic hand gestures. sEMG data were collected and preprocessed from 12 healthy subjects, with short-time Fourier transform (STFT) used to generate time-frequency images. Five CNN models with different depths were constructed for sEMG gesture recognition. Experimental results showed that the 4-layer deep CNN model (CNN4) performed the best in 9 gesture classification tasks, achieving an accuracy of 96.88%. Among classic CNN architectures, most models achieved classification accuracy above 90%, with DenseNet performing the best at 96.81%. Additionally, the study investigated the impact of time-frequency image resolution and STFT window function time window length on CNN performance. The results demonstrated that appropriate time-frequency image resolution and window length significantly improved CNN recognition accuracy. This method successfully improves gesture recognition accuracy and real-time performance by optimizing the input of sEMG time-frequency images and CNN network structure, contributing to the advancement of myoelectric prosthetic hand control systems.</span></p> |
| title | Gesture Recognition for Prosthetic Hand Control Using Surface Electromyography Time-Frequency Images and Convolutional Neural Networks (Demonstration video and paper data) |
| url | https://doi.org/10.5281/zenodo.15421098 |