Improvement of hand functions of spinal cord injury patients with electromyography-driven hand exoskeleton: A feasibility study
Improvement of hand functions of spinal cord injury patients with electromyography-driven hand exoskeleton: A feasibility study
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DOI：
10.1017/wtc.2020.9
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发表时间：
2021-01-05
影响因子：
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通讯作者：
Deshpande, Ashish D.
中科院分区：
文献类型：
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作者：
Yun, Youngmok;Na, Youngjin;Deshpande, Ashish D.
我们开发了一种独一无二的手部外骨骼，称为Maestro，它可以为那些幸存的严重残疾者提供手指运动的动力，以使用顺应性关节完成日常任务。在本文中，我们提出的结果从肌电图（EMG）控制策略进行脊髓损伤（SCI）患者（C5，C6和C7），其中受试者完成日常任务控制大师与肌电图信号从他们的前臂肌肉。凭借其顺应性驱动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体（标准化设置中超过15个）。为了生成Maestro的控制命令，实现了人工神经网络算法沿着概率控制方法，以利用从前臂和手掌测量的三个EMG信号对四个手部姿势进行鲁棒地分类和传递。标准化测试（称为Sollerman手功能测试）的分数增加，以及抓握的不同方面（如力量）的增强，表明Maestro能够改善SCI受试者的手功能。
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.