NDLI: Upper limb rehabilitation robot integrated with motion intention recognition and virtual reality environment

Content Provider	IEEE Xplore Digital Library
Author	Wu Jun Huan Jian Wang Yongji
Copyright Year	2010
Description	Author affiliation: Key Laboratory of Image Processing and Intelligent Control, Department of Control Science & Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China (Wu Jun; Huan Jian; Wang Yongji)
Abstract	Robotic-assisted therapy is of benefit to the recovery of upper limb motor function for the patients survived stoke. Whereas, there are few emphases on the patients' motion intention during the rehabilitation process. The goal of this study is to combine the control strategies based on patients' motion intention with an upper limb rehabilitation robot to improve the recovery for the patients. In this paper, we propose an integrated robot-assist rehabilitation system, in which a 3 degree-of-freedom (DOF) exoskeletal rehabilitation robot, an EMG-based intention recognition module and a VR game environment are seamlessly combined. According toharacteristics of EMG signals, the wavelet package analysis approach is applied to extract the features of EMG. The node energy is used to construct the feature vector instead of the original coefficients of wavelet package decomposition to resolve the time-invariance problem. Then feature projection results in the singularity problem of with-in scatter matrix during the feature dimension reduction. To overcome the disadvantage of the with-in scatter matrix, this paper uses a recursive algorithm which is proposed in our previous work. The reduced feature vector is recognized by a neural network classifier and the output of the classifier is used for the control inputs. Preliminary experiments are also performed to implement the control of the rehabilitation robotic system by using the proposed EMG reorganization method, together with a dart game realized in the virtual reality environment. Experimental results show that the performance of motion intention recognition is satisfactory and the entire integrated system is feasible.
Starting Page	3709
Ending Page	3715
File Size	818487
Page Count	7
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Wavelet Packet Games Feature extraction Electromyography Wavelet packets Classification algorithms Rehabilitation Robot Robots Motion Intention Recognition Virtual Reality
Content Type	Text
Resource Type	Article

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