神经康复
可用性
可穿戴计算机
计算机科学
机器人
自动化
康复
大数据
人机交互
可穿戴技术
外骨骼
分析
风险分析(工程)
数据科学
工程类
模拟
嵌入式系统
人工智能
医学
物理疗法
机械工程
操作系统
作者
Wenhao Deng,Ioannis Papavasileiou,Zhi Qiao,Wenlong Zhang,Alfred K. Lam,Song Han
出处
期刊:IEEE Reviews in Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2018-01-01
卷期号:11: 289-305
被引量:55
标识
DOI:10.1109/rbme.2018.2830805
摘要
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.
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