A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG.

Yumiao Chen,Yangliang Wen,Zhongliang Yang

doi:10.3390/s21020578

Yumiao Chen, Yangliang Wen + Show 1 more

Open Access

https://doi.org/10.3390/s21020578

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Abstract

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

Highlights

As a cardiovascular disease, stroke is common in the elderly
We developed a soft exoskeleton glove (SExoG) for hand bilateral training, which can be used to train injured fingers of wearers to complete four types of hand motion: Extension, rest, spherical grip, and fist
The number motion loops objective of Experiment 3 is to evaluate whether the systemofcan mirror the recognized from the whole motion sequence

Summary

Introduction

It often occurs with paralysis, which inhibits the motor ability of the patients. Loss of the ability to move the hands and fingers can reduce one’s quality of daily life considerably [1]. It has been demonstrated that rehabilitation training using the specified assistant systems can help regain function in the affected limb [2]. Most stroke patients have a strong demand for follow-up continuous care at home. Professional rehabilitation therapy often costs a lot and is not suitable for ordinary patients [3,4]. There is an urgent need for a system that can make hand rehabilitation more accessible and comfortable

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