Miniaturized Wearable Ultrasound System for Simultaneous Prediction of Wrist Angle and Grip Force During Dynamic Reaching.

Afsana Hossain Rima, Zahra Taghizadeh, Ahmed Bashatah, Abhishek Aher, Siddhartha Sikdar
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引用次数: 0

Abstract

Predicting grip force and wrist angle during dynamic hand movements is crucial for advancing upper-limb prosthetic systems, enabling simultaneous and proportional control of multiple degrees of freedom (DOFs). This study introduces a novel wearable ultrasound-based system that leverages M-mode data from four single-element transducers placed on the forearm to capture muscle activity for the concurrent prediction of grip force and wrist angle. A multi-layer perceptron (MLP) regressor was utilized for the simultaneous prediction of both parameters, and a comparative analysis was conducted using a Gaussian process regressor (GPR), which is commonly adopted previously in similar studies. The system was validated on unseen data from five participants without limb loss. The MLP demonstrated superior performance compared to GPR, achieving $\mathbf{R}^{\mathbf{2}}$ values of $0.85 \pm 0.06$ for wrist angle prediction and $0.74 \pm 0.07$ for grip force. These findings underscore the challenges of predicting simultaneous grip force and wrist angle during dynamic hand movements and highlight the need to address these issues for intuitive and practical prosthetic control in real-world scenarios.

同时预测腕部角度和握持力的微型可穿戴超声系统。
预测手部动态运动中的握力和手腕角度对于推进上肢假肢系统至关重要,从而实现多个自由度(dof)的同步和比例控制。本研究介绍了一种新型的基于超声波的可穿戴系统,该系统利用放置在前臂上的四个单元件传感器的m模式数据来捕获肌肉活动,以同时预测握力和手腕角度。利用多层感知器(MLP)回归量对两个参数进行同时预测,并使用高斯过程回归量(GPR)进行对比分析,这是之前类似研究中常用的方法。该系统在五名没有肢体丧失的参与者的未见数据上进行了验证。与GPR相比,MLP表现出优异的性能,手腕角度预测的$\mathbf{R}^{\mathbf{2}}$值为$0.85 \pm 0.06$,握力预测的$0.74 \pm 0.07$。这些发现强调了在动态手部运动中预测同时握力和手腕角度的挑战,并强调了在现实场景中直观和实用的假肢控制需要解决这些问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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