Estimation of finger joint angles from sEMG using a recurrent neural network with time-delayed input vectors

M. Hioki, Haruhisa Kawasaki
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引用次数: 26

Abstract

This paper reports a new technique for estimating continuous finger joint angles from surface electromyogram (sEMG). Using an artificial neural network including a feedback stream (recurrent structure) and a time-delay factor for input, continuous angles scaled to range between 0 and 1 are able to estimated with network from feature vectors. Feature vectors extracted from sEMG are scaled to range between 0 and 10. Target hand motions are free state, fist with five fingers, grip with four fingers except thumb, and thumb flexion only. In this paper, two types of estimation networks are compared. The type 1 network is an older system that cannot be used to train the dynamics of estimation system. The type 2 network is a newer system that can train the dynamics with a recurrent structure by a feedback stream and time-delay factor for input. A comparing of the two types networks show that estimations of finger joint angles with type 2 are better than those with type 1. In particular, the results from type 2 are better than those from type 1 at the transition from one motion to another motion.
基于递归神经网络的手指面肌电信号关节角度估计
本文报道了一种利用表面肌电图估计手指关节连续角的新方法。使用包含反馈流(循环结构)和输入时滞因子的人工神经网络,可以从特征向量中估计出0到1范围内的连续角度。从表面肌电信号中提取的特征向量被缩放到0到10之间。目标手的动作是自由状态,握拳用五个手指,握拳用除拇指以外的四个手指,并且只弯曲拇指。本文对两种估计网络进行了比较。1型网络是一个较老的系统,不能用于训练估计系统的动力学。2型网络是一种较新的系统,它可以通过反馈流和时滞因子作为输入来训练具有循环结构的动态。两种网络的比较表明,2型网络对手指关节角的估计优于1型网络。特别是,在从一个动作过渡到另一个动作时,类型2的结果优于类型1。
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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