TY - JOUR
T1 - A Wearable Hand Rehabilitation System with Soft Gloves
AU - Chen, Xiaoshi
AU - Gong, Li
AU - Wei, Liang
AU - Yeh, Shih Ching
AU - Da Xu, Li
AU - Zheng, Lirong
AU - Zou, Zhuo
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This article presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the nonaffected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real time.
AB - Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This article presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the nonaffected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real time.
KW - Hand rehabilitation
KW - machine learning (ML)
KW - mirror therapy
KW - soft glove
KW - task-oriented therapy
KW - wearable system
UR - http://www.scopus.com/inward/record.url?scp=85096724795&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.3010369
DO - 10.1109/TII.2020.3010369
M3 - 期刊論文
AN - SCOPUS:85096724795
SN - 1551-3203
VL - 17
SP - 943
EP - 952
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 2
M1 - 9146885
ER -