用肌电信号实时控制假手指握力，提升假肢操作自然度。
Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
- 通过肌电信号训练神经网络，实时估算指尖受力
- 十人实验验证力估计算法有效，四人在线试用控制精准
- 适合早期测试假肢功能，为截肢者提供直观控制方案
部分手部截肢严重影响个体身心福祉，而外置动力假肢的直观控制仍是未解难题。为此，我们开发了一种基于肌电信号（EMG）的力控假手指原型，以腕托为基础结构，作为靠近食指的额外手指，便于在健全受试者中开展早期评估。采用神经网络模型从EMG输入中估计指尖受力，实现对假手指抓握力度的在线调节。十名参与者实验验证了该力估计算法的有效性；四名佩戴假肢的用户在线测试也展示了精确控制能力。研究结果表明，基于EMG的力估计可显著提升假手指的功能表现。
原文摘要 · Abstract (English)
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.
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