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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.14658 |
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| _version_ | 1866910957928710144 |
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| author | Rolandino, Giovanni Zangrandi, Chiara Vieira, Taian Cerone, Giacinto Luigi Andrews, Brian FitzGerald, James J. |
| author_facet | Rolandino, Giovanni Zangrandi, Chiara Vieira, Taian Cerone, Giacinto Luigi Andrews, Brian FitzGerald, James J. |
| contents | This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation Rolandino, Giovanni Zangrandi, Chiara Vieira, Taian Cerone, Giacinto Luigi Andrews, Brian FitzGerald, James J. Signal Processing This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications. |
| title | HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.14658 |