TY - JOUR
T1 - Advances in Automation Technologies for Lower Extremity Neurorehabilitation
T2 - A Review and Future Challenges
AU - Deng, Wenhao
AU - Papavasileiou, Ioannis
AU - Qiao, Zhi
AU - Zhang, Wenlong
AU - Lam, Kam Yiu
AU - Han, Song
N1 - Publisher Copyright:
© 2008-2011 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - 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.
AB - 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.
KW - Assistive robots
KW - big data analytics platforms
KW - biofeedback
KW - disease diagnosis and analysis
KW - gait quantification
KW - lower extremity neurorehabilitation
KW - wearable sensors
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U2 - 10.1109/RBME.2018.2830805
DO - 10.1109/RBME.2018.2830805
M3 - Review article
C2 - 29994006
AN - SCOPUS:85046491756
SN - 1937-3333
VL - 11
SP - 289
EP - 305
JO - IEEE Reviews in Biomedical Engineering
JF - IEEE Reviews in Biomedical Engineering
M1 - 8354783
ER -