筋電信号からの把持力の学習とオンライン再現による義指制御
Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
筋電信号から指先力を推定するニューラルネットモデルを構築し、義指の把持力をオンラインで調整できることを10名の実験で示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Robin Arbaud, Elisa Motta, Marco Domenico Avaro, Stefano Picinich, Marta Lorenzini, Arash Ajoudani
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
