Instrumentation Design of Game Rehabilitation with Myoelectric Command

Ni Wayan Yulya Wiani, A. Arifin, M. Fatoni, Josaphat Pramudijanto
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Abstract

Stroke is a potentially fatal illness caused by clotting of the blood vessels that supply oxygen to the brain. Up to 65 percent of stroke patients are affected by Hemiparesis. Muscle weakness is a typical side effect, which might lead to a reduction in physical activity. This makes it difficult for post-stroke patients to carry out daily tasks. Therefore, a game-based rehabilitation strategy focused on grasping movement is recommended to help the upper limbs recover. Individual biomedical signals were used to control the game. EMG instrumentation used to process biomedical signals. To aid in this process, hand gloves are also used to evaluate the range of motion produced during rehabilitation. The game becomes more exciting by using Leap Motion to track patient hand movements and move virtual hands in the game. The experimental results revealed an average increase in the amplitude of the LEMG signal generated by participants 1 and 2. The average amplitude increase in subject 1 was 22.81 mV, while it was 89.60 mV in subject 2. For further research, a compact and sensitive EMG instrumentation can be built. In addition, real-time computing can be used to build rehabilitation systems that can detect the onset of LEMG and create more interactive games.
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基于肌电指令的游戏康复仪器设计
中风是一种潜在的致命疾病,由向大脑供氧的血管凝结引起。高达65%的中风患者患有偏瘫。肌肉无力是一种典型的副作用,可能导致体力活动减少。这使得中风后患者难以完成日常任务。因此,我们推荐一种基于游戏的康复策略,侧重于抓取运动,以帮助上肢恢复。个体生物医学信号被用来控制游戏。用于处理生物医学信号的肌电图仪器。为了在这个过程中提供帮助,手套也用于评估康复过程中产生的运动范围。通过使用Leap Motion来跟踪病人的手部动作,并在游戏中移动虚拟的手,游戏变得更加令人兴奋。实验结果显示,参与者1和参与者2产生的LEMG信号幅度平均增加。受试者1的平均增幅为22.81 mV,受试者2的平均增幅为89.60 mV。为了进一步的研究,可以建立一个紧凑、灵敏的肌电图仪器。此外,实时计算可以用来建立康复系统,可以检测LEMG的发作,并创建更多的互动游戏。
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