Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913820443672576 |
|---|---|
| author | Arbaud, Robin Motta, Elisa Avaro, Marco Domenico Picinich, Stefano Lorenzini, Marta Ajoudani, Arash |
| author_facet | Arbaud, Robin Motta, Elisa Avaro, Marco Domenico Picinich, Stefano Lorenzini, Marta Ajoudani, Arash |
| contents | Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control Arbaud, Robin Motta, Elisa Avaro, Marco Domenico Picinich, Stefano Lorenzini, Marta Ajoudani, Arash Robotics Machine Learning Systems and Control Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers. |
| title | Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2505.02574 |