A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG
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 ha...
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doaj-de15ad4a091b45529aa9d73d443fdfbc2021-01-16T00:01:11ZengMDPI AGSensors1424-82202021-01-012157857810.3390/s21020578A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMGYumiao Chen0Zhongliang Yang1Yangliang Wen2School of Art, Design and Media, East China University of Science and Technology, Shanghai 200237, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaTraditional 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.https://www.mdpi.com/1424-8220/21/2/578exoskeletonsurface electromyographyhand motion recognitionbilateral training |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yumiao Chen Zhongliang Yang Yangliang Wen |
spellingShingle |
Yumiao Chen Zhongliang Yang Yangliang Wen A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG Sensors exoskeleton surface electromyography hand motion recognition bilateral training |
author_facet |
Yumiao Chen Zhongliang Yang Yangliang Wen |
author_sort |
Yumiao Chen |
title |
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG |
title_short |
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG |
title_full |
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG |
title_fullStr |
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG |
title_full_unstemmed |
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG |
title_sort |
soft exoskeleton glove for hand bilateral training via surface emg |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-01-01 |
description |
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. |
topic |
exoskeleton surface electromyography hand motion recognition bilateral training |
url |
https://www.mdpi.com/1424-8220/21/2/578 |
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