Impact of Electrode Position on Forearm Orientation Invariant Hand Gesture Recognition
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912052618985472 |
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| author | Islam, Md. Johirul Rumman, Umme Ferdousi, Arifa Pervez, Md. Sarwar Ara, Iffat Ahmad, Shamim Haque, Fahmida Hamid, Sawal Ali, Md. Zaman, Kh Shahriya Reaz, Mamun Bin Ibne Chowdhury, Mustafa Habib Islam, Md. Rezaul |
| author_facet | Islam, Md. Johirul Rumman, Umme Ferdousi, Arifa Pervez, Md. Sarwar Ara, Iffat Ahmad, Shamim Haque, Fahmida Hamid, Sawal Ali, Md. Zaman, Kh Shahriya Reaz, Mamun Bin Ibne Chowdhury, Mustafa Habib Islam, Md. Rezaul |
| contents | Objective: Variation of forearm orientation is one of the crucial factors that drastically degrades the forearm orientation invariant hand gesture recognition performance or the degree of freedom and limits the successful commercialization of myoelectric prosthetic hand or electromyogram (EMG) signal-based human-computer interfacing devices. This study investigates the impact of surface EMG electrode positions (elbow and forearm) on forearm orientation invariant hand gesture recognition. Methods: The study has been performed over 19 intact limbed subjects, considering 12 daily living hand gestures. The quality of the EMG signal is confirmed in terms of three indices. Then, the recognition performance is evaluated and validated by considering three training strategies, six feature extraction methods, and three classifiers. Results: The forearm electrode position provides comparable to or better EMG signal quality considering three indices. In this research, the forearm electrode position achieves up to 5.35% improved forearm orientation invariant hand gesture recognition performance compared to the elbow electrode position. The obtained performance is validated by considering six feature extraction methods, three classifiers, and real-time experiments. In addition, the forearm electrode position shows its robustness with the existence of recent works, considering recognition performance, investigated gestures, the number of channels, the dimensionality of feature space, and the number of subjects. Conclusion: The forearm electrode position can be the best choice for getting improved forearm orientation invariant hand gesture recognition performance. Significance: The performance of myoelectric prosthesis and human-computer interfacing devices can be improved with this optimized electrode position. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of Electrode Position on Forearm Orientation Invariant Hand Gesture Recognition Islam, Md. Johirul Rumman, Umme Ferdousi, Arifa Pervez, Md. Sarwar Ara, Iffat Ahmad, Shamim Haque, Fahmida Hamid, Sawal Ali, Md. Zaman, Kh Shahriya Reaz, Mamun Bin Ibne Chowdhury, Mustafa Habib Islam, Md. Rezaul Human-Computer Interaction Signal Processing Objective: Variation of forearm orientation is one of the crucial factors that drastically degrades the forearm orientation invariant hand gesture recognition performance or the degree of freedom and limits the successful commercialization of myoelectric prosthetic hand or electromyogram (EMG) signal-based human-computer interfacing devices. This study investigates the impact of surface EMG electrode positions (elbow and forearm) on forearm orientation invariant hand gesture recognition. Methods: The study has been performed over 19 intact limbed subjects, considering 12 daily living hand gestures. The quality of the EMG signal is confirmed in terms of three indices. Then, the recognition performance is evaluated and validated by considering three training strategies, six feature extraction methods, and three classifiers. Results: The forearm electrode position provides comparable to or better EMG signal quality considering three indices. In this research, the forearm electrode position achieves up to 5.35% improved forearm orientation invariant hand gesture recognition performance compared to the elbow electrode position. The obtained performance is validated by considering six feature extraction methods, three classifiers, and real-time experiments. In addition, the forearm electrode position shows its robustness with the existence of recent works, considering recognition performance, investigated gestures, the number of channels, the dimensionality of feature space, and the number of subjects. Conclusion: The forearm electrode position can be the best choice for getting improved forearm orientation invariant hand gesture recognition performance. Significance: The performance of myoelectric prosthesis and human-computer interfacing devices can be improved with this optimized electrode position. |
| title | Impact of Electrode Position on Forearm Orientation Invariant Hand Gesture Recognition |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2410.00029 |