Template matching based motion classification for unsupervised post-stroke rehabilitation

Zhe Zhang, Qiang Fang, Liuping Wang, Peter Barrett
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引用次数: 19

Abstract

Post-stroke rehabilitation training is proven clinically to be essential and effective on helping stroke patients to regain part of the functionality of their body. In recent years, cost-efficient rehabilitation training programs especially unsupervised training have become a popular research area due the increasing number of post-stroke hospitalizations and the high healthcare expenditure associated. In order to achieve unsupervised rehabilitation training, a reliable continuous monitoring measure is crucial. This paper proposed a motion classification system based on template matching technique that can constantly identify and record the quantity and quality of patient's rehabilitation exercise as a reference for the professionals to analyze patient's recovery progress. It can also integrate features like fall detection to improve safety in unsupervised training environment. In contrast to the conventional motion tracking system which are generally expensive and complicated to operate, the proposed system uses only low-cost non-visual based wireless sensors for acceleration data collection. Since the classification process is based on template matching, there are no additional sensors like gyroscope required for precise reconstruction of patient's motion. To test the system performance, a preliminary experiment involving an actual stroke patient has been conducted. Despite the movement performed by the patient was non-standard and inconsistent, the system was still able to identify the predefined exercises from a series of movements and count the number of repetition for each exercise accurately.
基于模板匹配的无监督脑卒中后康复运动分类
中风后康复训练在帮助中风患者恢复部分身体功能方面被临床证明是必要和有效的。近年来,由于卒中后住院治疗人数的增加和相关的高额医疗费用,成本效益康复训练方案,特别是无监督训练已成为一个热门的研究领域。为了实现无监督的康复训练,可靠的连续监测措施至关重要。本文提出了一种基于模板匹配技术的运动分类系统,可以持续识别和记录患者康复运动的数量和质量,作为专业人员分析患者康复进展的参考。它还可以集成跌倒检测等功能,以提高无监督训练环境中的安全性。与传统的运动跟踪系统相比,传统的运动跟踪系统通常昂贵且操作复杂,该系统仅使用低成本的非视觉无线传感器来收集加速度数据。由于分类过程是基于模板匹配,因此不需要陀螺仪等额外的传感器来精确重建患者的运动。为了测试系统的性能,进行了一个涉及实际中风患者的初步实验。尽管患者的动作不规范且不一致,但系统仍然能够从一系列动作中识别出预定义的运动,并准确地计算每个运动的重复次数。
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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