Implementation of Myo Armband on Mobile Application for Post-stroke Patient Hand Rehabilitation

T. Dewantoro, R. Sigit, Heny Yuniarti, Yudith Dian Prawitri, Fridastya Andini Pamudyaningrum, M. I. Awal
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引用次数: 1

Abstract

Medical rehabilitation is one of the efforts to restore motor function of post-stroke patients, but the biggest factor that makes patients quickly restore motor function by active patient movement exercises. The movement in question is the movement carried out every day outside medical rehabilitation at the hospital. On the other hand, patients are reluctant to do therapy independently outside the hospital, because there is no tool that supports patients to do so. So, we need a device that helps patients to do therapy independently. The device is connected to Myo Armband to read the gestures of the patient by looking at the EMG signal from the patient's hand. Then the system performs matching gestures during therapy with EMG signal data that has been trained. The motion matching is done by calculating the Euclidean distance between the two EMG signal data obtained from the Myo Armband device. From the results of the tests carried out, the accuracy of movement matching results obtained an average accuracy of 89.67 percent for flexion-extension gestures and 82 percent for pronation-supination gestures. It can be concluded that Myo Armband in the Mobile Application can be used for Rehabilitation of post stroke patient hands.
Myo臂环在脑卒中后手部康复移动应用中的实现
医学康复是脑卒中后患者运动功能恢复的努力之一,但通过积极的患者运动锻炼使患者迅速恢复运动功能的最大因素。所讨论的运动是每天在医院医疗康复之外进行的运动。另一方面,患者不愿意在医院外独立进行治疗,因为没有工具支持患者这样做。所以,我们需要一种能帮助病人独立进行治疗的设备。该设备与Myo臂带相连,通过观察患者手部的肌电图信号来读取患者的手势。然后,系统在治疗过程中根据训练好的肌电图信号数据执行匹配手势。运动匹配是通过计算从Myo Armband装置获得的两个肌电信号数据之间的欧几里德距离来完成的。从所进行的测试结果来看,运动匹配结果的平均准确率对于屈伸手势为89.67%,对于旋前手势为82%。综上所述,Myo臂章移动应用程序可以用于脑卒中后患者手部康复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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