Impulsive pattern recognition of a myoelectric hand via Dynamic Time Warping
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915252481818624 |
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| author | Kadilar, Mustafa Can Toptaş, Ersin Akgün, Gazi |
| author_facet | Kadilar, Mustafa Can Toptaş, Ersin Akgün, Gazi |
| contents | Although myoelectric prosthetic hands provide amputees with intuitive control, their reliance on many EMG sensors limits accessibility and makes them complex and expensive. To address this problem, this work presents a different perspective that makes use of a single EMG sensor and brief impulse signals in conjunction with Dynamic Time Warping (DTW) for accurate pattern detection. Conventional techniques rely on real-time data from multiple sensors, which can be costly and bulky. The method presents high accuracy while lowering hardware complexity and expense. A DTW-based system that reliably identifies muscle activation patterns from short EMG signals was created and tested. Results show that this single-sensor approach obtained an accuracy rate of 92%, which is similar to that of conventional multi-sensor systems. This research provides a more straightforward and economical approach that can be used to obtain enhanced myoelectric control. These findings provide a different perspective on more easily accessible and user-friendly prosthetic devices, which will be especially helpful in disaster-affected areas where quick deployment is essential. Future improvements would investigate this system's dependability over time and wider implementations in real situations, to take prosthetic technology one step further. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Impulsive pattern recognition of a myoelectric hand via Dynamic Time Warping Kadilar, Mustafa Can Toptaş, Ersin Akgün, Gazi Biological Physics I.5.1; I.5.4; I.2.9 Although myoelectric prosthetic hands provide amputees with intuitive control, their reliance on many EMG sensors limits accessibility and makes them complex and expensive. To address this problem, this work presents a different perspective that makes use of a single EMG sensor and brief impulse signals in conjunction with Dynamic Time Warping (DTW) for accurate pattern detection. Conventional techniques rely on real-time data from multiple sensors, which can be costly and bulky. The method presents high accuracy while lowering hardware complexity and expense. A DTW-based system that reliably identifies muscle activation patterns from short EMG signals was created and tested. Results show that this single-sensor approach obtained an accuracy rate of 92%, which is similar to that of conventional multi-sensor systems. This research provides a more straightforward and economical approach that can be used to obtain enhanced myoelectric control. These findings provide a different perspective on more easily accessible and user-friendly prosthetic devices, which will be especially helpful in disaster-affected areas where quick deployment is essential. Future improvements would investigate this system's dependability over time and wider implementations in real situations, to take prosthetic technology one step further. |
| title | Impulsive pattern recognition of a myoelectric hand via Dynamic Time Warping |
| topic | Biological Physics I.5.1; I.5.4; I.2.9 |
| url | https://arxiv.org/abs/2504.15256 |