基于手部运动传感器的自我肢体康复系统

S. M. S. Nugroho, M. Fauzan, I. Purnama
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引用次数: 1

摘要

中风是造成印度尼西亚大量残疾和死亡率的疾病之一。在印度尼西亚几乎所有医院中,中风死亡人数达到15.4%。帮助脑卒中患者改善运动功能、言语、认知等功能受损的步骤之一是进行一系列脑卒中后康复。中风后的康复可以在物理治疗师的直接监督下进行,也可以在家中单独进行,或者通常称为自我康复。中风后康复有多种动作训练,其中一种就是手指的运动。MedCap作为一种工具出现,可以帮助物理治疗师和患者恢复中风后的手指运动。MedCap采用Leap Motion手部运动传感器,可以记录预定的参考动作,成功率为62.35%。MedCap还可以使用欧几里得距离法计算参考运动与患者实时运动的一致性,作为对物理治疗师和患者的反馈形式。MedCap采用的方法可以计算出运动与实时运动的符合性,平均值为43.2495%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Physical Rehabilitation System based on Hand Motion Sensor
Stroke is one of the diseases that cause high number of disability and mortality in Indonesia. The number of deaths from stroke in Indonesia reached 15.4% in almost all hospitals in Indonesia. One of the steps to help stroke patients to improve their motor function, speech, cognition, and other impaired functions is to conduct a series of post-stroke rehabilitation. Post-stroke rehabilitation can be done with direct supervision of the physiotherapist or performed alone at home or commonly called self-rehabilitation. Post-stroke rehabilitation has a variety of movements training, one of which is the movement of a finger. MedCap emerged as one of the tools that can help physiotherapists and patients to rehabilitate post-stroke fingers movement. MedCap which uses Leap Motion hand motion sensor can record a predetermined reference movement with a success rate of 62.35%. MedCap can also calculate the conformity of the reference movement and the real time movement of the patient using the Euclidean Distance method as a form of feedback to the physiotherapist and patient. The method used by MedCap can calculate the conformity of movement with real time movement with an average value of 43.2495%.
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