14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand

Daiki Suzuki, Yusuke Yamanoi, H. Yamada, Ko Wakita, R. Kato, H. Yokoi
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Abstract

A stationary grasping posture is classified in the control method of an electromyogram prosthetic hand. This grasping posture is static, such as an open hand posture, and one in which the operator of an electromyogram prosthetic hand intentionally continues muscular contraction. In classifying the stationary grasping posture, a movement delay of the robot hand occurs, which feels unnaturally to the operator. To solve these problems, authors propose a method that predicts a grasping posture using the surface electromyogram (sEMG) of low muscle contraction power in hand pre-shaping. In this paper, our research on the performance of grasping posture classification using sEMG for naturally reaching for and grasping an object is presented. Experimental results demonstrate that when the sEMG amplitude peaks in hand pre-shaping, it is useful in classifying the grasping posture.
14. 基于手预成型的肌电信号抓取姿势分类,实现肌电手的自然控制
在肌电义手控制方法中,对静止抓取姿势进行了分类。这种抓握姿势是静态的,如张开的手姿势,在这种姿势中,肌电图假手的操作者有意地继续肌肉收缩。在对静止抓取姿势进行分类时,会产生机械手的运动延迟,这对操作者来说是不自然的。为了解决这些问题,作者提出了一种利用手部预成型中低肌肉收缩力的肌表电图(sEMG)预测抓取姿势的方法。本文研究了基于表面肌电信号的抓取姿势分类在自然抓取物体中的性能。实验结果表明,在手部预整形中,当表面肌电信号振幅达到峰值时,可用于抓取姿势的分类。
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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