基于imu的运动捕捉系统的康复应用:系统综述

Chenyu Gu , Weicong Lin , Xinyi He, Lei Zhang, Mingming Zhang
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引用次数: 2

摘要

近年来,基于惯性测量单元(IMU)的运动捕捉(Mocap)系统在康复中的应用显著增长。本文旨在概述目前康复领域中基于IMU的Mocap系统设计,探索这些系统的具体应用和实现,并讨论考虑传感器限制的潜在未来发展。在这篇综述中,2013年至2022年,使用Scopus、IEEE Xplore、PubMed和Web of Science进行了系统的文献检索。共纳入65项研究,并根据其康复应用、目标人群、系统部署和测量进行分析。康复评估、训练和两者的比例分别为82%、12%和6%。结果表明,研究的主要焦点是中风,这是最常见的病理学疾病之一。此外,还广泛检查了不针对特定病理的一般康复,特别强调步态分析。步态分析最常见的传感器配置是两个测量下肢时空参数的IMU。然而,缺乏训练应用和上肢研究可能归因于电池寿命有限和传感器漂移。为了解决这个问题,使用低功耗芯片和低功耗传输路径是延长长期训练使用时间的一种潜在方式。此外,我们建议开发一个具有传感器融合的高度集成的多模态系统。
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IMU-based motion capture system for rehabilitation applications: A systematic review

In recent years, the use of inertial measurement unit (IMU)-based motion capture (Mocap) systems in rehabilitation has grown significantly. This paper aimed to provide an overview of current IMU-based Mocap system designs in the field of rehabilitation, explore the specific applications and implementation of these systems, and discuss potential future developments considering sensor limitations. For this review, a systematic literature search was conducted using Scopus, IEEE Xplore, PubMed, and Web of Science from 2013 to 2022. A total of 65 studies were included and analyzed based on their rehabilitation application, target population, and system deployment and measurement. The proportion of rehabilitation assessment, training, and both were 82%, 12%, and 6% respectively. The results showed that primary focus of the studies was stroke that was one of the most commonly studied pathological disease. Additionally, general rehabilitation without targeting a specific pathology was also examined widely, with a particular emphasis on gait analysis. The most common sensor configuration for gait analysis was two IMUs measuring spatiotemporal parameters of the lower limb. However, the lack of training applications and upper limb studies could be attributed to the limited battery life and sensor drift. To address this issue, the use of low-power chips and low-consumption transmission pathways was a potential way to extend usage time for long-term training. Furthermore, we suggest the development of a highly integrated multi-modal system with sensor fusion.

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