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sEMG Signal and Hill Model based Continuous Prediction for Hand Grasping Motion

机译：基于SEMG信号和山坡模型的手工抓握运动

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摘要

This paper is aimed at the continuous hand grasping motion prediction during all fingers flexion and extension. Only sEMG signals recorded from flexor digitorum superficialis and extensor digitorum of forearm are used to predict the flexion and extension motion. In order to find the relation between sEMG signals and hand grasping motion, a Hill model is used to represent the force value of the muscles. Some assumptions are also made for simplicity in calculating the association. A simple and efficient motion recording system using flex sensor, Mtx sensor and a glove is designed for the purpose of recording fingers motion. The motions are voluntary finger flexion and extension with no load. Acceptable results are achieved. The purpose of this paper is to provide a method for continuous hand grasping motion prediction based on sEMG signals. Although some assumptions are made to simplify the problem and indeed these assumptions brought prediction errors in the experiment, the method shows itself an alternative way to use sEMG signals for hand motion prediction.
机译：本文旨在在所有手指屈伸和延伸期间的连续手抓取运动预测。只有从屈肌位数和前臂的伸展位数字记录的SEMG信号用于预测屈曲和延伸运动。为了找到SEMG信号和手动抓握运动之间的关系，山坡模型用于表示肌肉的力值。在计算关联时也是为了简单起见的一些假设。使用Flex传感器，MTX传感器和手套的简单高效的运动记录系统是为了记录手指运动而设计的。动作是自愿手指屈曲和延伸，没有负载。实现了可接受的结果。本文的目的是提供一种基于SEMG信号的连续手动抓握运动预测的方法。虽然进行了一些假设来简化问题，但实际上这些假设在实验中带来了预测误差，但该方法显示了使用用于手动预测的SEMG信号的替代方法。

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