The Cybernetic Rehabilitation Aid: A novel concept for direct rehabilitation

The evaluation and teaching of motor skills in relation to patients are two important aspects of motor skill training. These two points or problems must be resolved in order to make such training effective. To address the issues simultaneously within a single system, this study proposes a Cybernetic Rehabilitation Aid (CRA) under the concept of direct teaching using tactile feedback with an EMG-based motor skill evaluation function. The CRA involves a human-machine-human (physiotherapist-rehabilitation robot-patient) interface known as a Cybernetic Interface Platform using biological signals not only to monitor patients' motor skills but also to directly teach such skills to them. The CIP can also be used as a human-human (physiotherapist-patient) system as well as a human-machine (physiotherapist-rehabilitation robot) system. In order to evaluate motor skills, the motions of the physiotherapist (T) and the patient (P) were analyzed using a loglinearized Gaussian mixture model that can classify motion patterns via electromyography (EMG) signals. Tactile stimulators were used to convey the instructions of the therapist or the system to the patients. A rehabilitation robot known as the Biodex System was integrated into the developed setup for a number of rehabilitation tasks.

[1]  N. Hoshimiya,et al.  Joint angle control by FES using a feedback error learning controller , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Toshio Tsuji,et al.  A log-linearized Gaussian mixture network and its application to EEG pattern classification , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[3]  Hiroshi Yamaguchi,et al.  EMG automatic switch for FES control for hemiplegics using artificial neural network , 2002, Robotics Auton. Syst..

[4]  G. Burdea Virtual Rehabilitation - Benefits and Challenges , 2003, Yearbook of Medical Informatics.

[5]  M. Hauschild,et al.  A Virtual Reality Environment for Designing and Fitting Neural Prosthetic Limbs , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Toshio Fukuda,et al.  Design and control of an exoskeleton system for human upper-limb motion assist , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[7]  M. Batavia,et al.  A do-it-yourself membrane-activated auditory feedback device for weight bearing and gait training: a case report. , 2001, Archives of physical medicine and rehabilitation.

[8]  Y. Takahashi,et al.  Basic experiments of upper limb rehabilitation using haptic device system , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[9]  Cynthia Breazeal,et al.  TIKL: Development of a Wearable Vibrotactile Feedback Suit for Improved Human Motor Learning , 2007, IEEE Transactions on Robotics.

[10]  Woon-fong Wallace Leung,et al.  Quantitative evaluation of motor functional recovery process in chronic stroke patients during robot-assisted wrist training. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[11]  Jacob Rosen,et al.  A myosignal-based powered exoskeleton system , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[12]  B. Brooks Route learning in a case of amnesia : A preliminary investigation into the efficacy of training in a virtual environment , 1999 .

[13]  A. Cliquet,et al.  Artificial sensorimotor integration in spinal cord injured subjects through neuromuscular and electrotactile stimulation. , 2000, Artificial organs.

[14]  Maureen K. Holden,et al.  Virtual Environments for Motor Rehabilitation: Review , 2005, Cyberpsychology Behav. Soc. Netw..

[15]  Shih-Ching Yeh,et al.  Tailoring virtual reality technology for stroke rehabilitation: a human factors design , 2006, CHI Extended Abstracts.

[16]  Nobuyuki Matsui,et al.  Basic study on rehabilitation support system for upper limb motor function , 2002, 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623).

[17]  P.E. Crago,et al.  Functional restoration of elbow extension after spinal-cord injury using a neural network-based synergistic FES controller , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  D.S. Andreasen,et al.  Exoskeleton with EMG based active assistance for rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[19]  P.E. Crago,et al.  Reciprocal EMG control of elbow extension by FES , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  C A Phillips,et al.  Walking while using a sensory tactile feedback system: potential use with a functional electrical stimulation orthosis. , 1991, Journal of biomedical engineering.