肌电图驱动的手外骨骼改善脊髓损伤患者手功能的可行性研究

IF 3.4 Q2 ENGINEERING, BIOMEDICAL
Wearable technologies Pub Date : 2021-01-05 eCollection Date: 2020-01-01 DOI:10.1017/wtc.2020.9
Youngmok Yun, Youngjin Na, Paria Esmatloo, Sarah Dancausse, Alfredo Serrato, Curtis A Merring, Priyanshu Agarwal, Ashish D Deshpande
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引用次数: 0

摘要

摘要我们开发了一种独一无二的手外骨骼,名为Maestro,它可以为那些患有严重残疾的人的手指运动提供动力,让他们使用顺从的关节完成日常任务。在本文中,我们介绍了对脊髓损伤(SCI)患者(C5、C6和C7)进行的肌电图(EMG)控制策略的结果,在该策略中,受试者用前臂肌肉的EMG信号完成了控制Maestro的日常任务。凭借其顺应性的动作和与自然手指运动相匹配的自由度,Maestro能够帮助受试者抓住和操纵各种日常物品(标准化套装中有15件以上)。为了为Maestro生成控制命令,实现了一种人工神经网络算法和概率控制方法,用前臂和手掌测量的三个EMG信号对四个手部姿势进行稳健分类和传递。一项名为Sollerman手功能测试的标准化测试的分数增加,以及在抓握的不同方面(如力量)的增强,表明Maestro能够改善SCI受试者的手功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement of hand functions of spinal cord injury patients with electromyography-driven hand exoskeleton: A feasibility study.

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.

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来源期刊
CiteScore
5.80
自引率
0.00%
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审稿时长
11 weeks
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