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自动化技术在下肢神经康复中的应用进展:综述与未来挑战

Advances in Automation Technologies for Lower Extremity Neurorehabilitation: A Review and Future Challenges.

出版信息

IEEE Rev Biomed Eng. 2018;11:289-305. doi: 10.1109/RBME.2018.2830805. Epub 2018 May 4.

Abstract

The world is experiencing an unprecedented, enduring, and pervasive aging process. With more people who need walking assistance, the demand for lower extremity gait rehabilitation has increased rapidly over the years. The current clinical gait rehabilitative training requires heavy involvement of both medical doctors and physical therapists, and thus, are labor intensive, subjective, and expensive. To address these problems, advanced automation techniques, especially along with the proliferation of smart sensing and actuation devices and big data analytics platforms, have been introduced into this field to make the gait rehabilitation convenient, efficient, and personalized. This survey paper provides a comprehensive review on recent technological advances in wearable sensors, biofeedback devices, and assistive robots. Empowered by the emerging networking and computing technologies in the big data era, these devices are being interconnected into smart and connected rehabilitation systems to provide nonintrusive and continuous monitoring of physical and neurological conditions of the patients, perform complex gait analysis and diagnosis, and allow real-time decision making, biofeedback, and control of assistive robots. For each technology category, a detailed comparison among the existing solutions is provided. A thorough discussion is also presented on remaining open problems and future directions to further improve the safety, efficiency, and usability of the technologies.

摘要

世界正在经历一场前所未有的、持久的、普遍的老龄化进程。随着需要助行的人越来越多,下肢步态康复的需求近年来迅速增长。目前的临床步态康复训练需要医生和物理治疗师的大量参与,因此劳动力密集、主观且昂贵。为了解决这些问题,先进的自动化技术,特别是随着智能传感和驱动设备以及大数据分析平台的普及,已经被引入到这个领域,以使步态康复变得方便、高效和个性化。本调查论文全面回顾了可穿戴传感器、生物反馈设备和辅助机器人方面的最新技术进展。在大数据时代新兴的网络和计算技术的支持下,这些设备被相互连接成智能和互联的康复系统,以提供对患者身体和神经状况的非侵入性和连续监测,进行复杂的步态分析和诊断,并允许实时决策、生物反馈和辅助机器人的控制。对于每个技术类别,我们提供了现有解决方案之间的详细比较。还对遗留的开放问题和未来方向进行了深入讨论,以进一步提高技术的安全性、效率和可用性。

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