TY - GEN
T1 - Design of Isometric and Isotonic Soft Hand for Rehabilitation Combining with Noninvasive Brain Machine Interface
AU - Li, Yue
AU - Zhang, Jinhua
AU - Zhang, Cheng
AU - Xiao, Yanqing
AU - Hong, Jun
AU - Wang, Michael Yu
AU - Li, Yanyi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/30
Y1 - 2018/6/30
N2 - Comparing with the traditional way for hand rehabilitation, such as simple trainers and artificial rigid auxiliary, this paper presents an isometric and isotonic soft hand for rehabilitation supported by the soft robots theory which aims to satisfy the more comprehensive rehabilitation requirements. Salient features of the device are the ability to achieve higher and controllable stiffness for both isometric and isotonic contraction. Then we analyze the active control for isometric and isotonic movement through electroencephalograph (EEG) signal. This paper focuses on three issues. The first is using silicon rubber to build a soft finger which can continuously stretch and bend to fit the basic action of the fingers. The second is changing stiffness of the finger through the coordination between variable stiffness cavity and actuating cavity. The last is to classify different EEG states based on isometric and isotonic contraction using common spatial pattern feature extraction (CSP) methods and support vector machine classification methods (SVM). On this basis, an EEG-based manipulator control system was set up.
AB - Comparing with the traditional way for hand rehabilitation, such as simple trainers and artificial rigid auxiliary, this paper presents an isometric and isotonic soft hand for rehabilitation supported by the soft robots theory which aims to satisfy the more comprehensive rehabilitation requirements. Salient features of the device are the ability to achieve higher and controllable stiffness for both isometric and isotonic contraction. Then we analyze the active control for isometric and isotonic movement through electroencephalograph (EEG) signal. This paper focuses on three issues. The first is using silicon rubber to build a soft finger which can continuously stretch and bend to fit the basic action of the fingers. The second is changing stiffness of the finger through the coordination between variable stiffness cavity and actuating cavity. The last is to classify different EEG states based on isometric and isotonic contraction using common spatial pattern feature extraction (CSP) methods and support vector machine classification methods (SVM). On this basis, an EEG-based manipulator control system was set up.
UR - https://www.scopus.com/pages/publications/85053513917
U2 - 10.1109/URAI.2018.8441816
DO - 10.1109/URAI.2018.8441816
M3 - 会议稿件
AN - SCOPUS:85053513917
SN - 9781538663349
T3 - 2018 15th International Conference on Ubiquitous Robots, UR 2018
SP - 749
EP - 754
BT - 2018 15th International Conference on Ubiquitous Robots, UR 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Ubiquitous Robots, UR 2018
Y2 - 27 June 2018 through 30 June 2018
ER -