ROBOT-ASSISTED STROKE REHABILITATION: JOINT TORQUE/FORCE CONVERSION FROM EMG USING GA PROCESS

This paper focuses on the implementation of robot-assisted stroke rehabilitation using electromyography (EMG) as the interface between the robot and subjects. The key issue in implementing EMG for this application is the conversion process of EMG signal into torque/force, which is used as a input to the control system. This paper presents a methodology of EMG signal conversion into estimated joint torque by using simulated annealing (SA) technique. Basic principle of SA, formulation, and implementation to the problem are discussed in this paper. Experimental studies with real life EMG data have been carried out for five subjects. These studies are used to evaluate the feasibility of the methodology proposed for robot-assisted stroke rehabilitation problem. Experimental investigations and results are discussed at the end of the paper.

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

[2]  N. Hogan,et al.  The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. , 1997, Archives of neurology.

[3]  Y. Koike,et al.  A myokinetic arm model for estimating joint torque and stiffness from EMG signals during maintained posture. , 2009, Journal of neurophysiology.

[4]  C. Burgar,et al.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.

[5]  David G. Lloyd,et al.  A real-time EMG-driven virtual arm , 2002, Comput. Biol. Medicine.

[6]  D. Lloyd,et al.  An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. , 2003, Journal of biomechanics.

[7]  Andreas Wege,et al.  Application of EMG signals for controlling exoskeleton robots , 2006, Biomedizinische Technik. Biomedical engineering.

[8]  D. Jette,et al.  The relation between therapy intensity and outcomes of rehabilitation in skilled nursing facilities. , 2005, Archives of physical medicine and rehabilitation.

[9]  T. Kuo,et al.  Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model. , 1999, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[10]  N. Hogan,et al.  Effects of robotic therapy on motor impairment and recovery in chronic stroke. , 2003, Archives of physical medicine and rehabilitation.

[11]  Rong Song,et al.  Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations , 2005, Medical and Biological Engineering and Computing.

[12]  N. Zheng,et al.  An analytical model of the knee for estimation of internal forces during exercise. , 1998, Journal of biomechanics.

[13]  G. Gini,et al.  An EMG-controlled exoskeleton for hand rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[14]  S. Parasuraman,et al.  Development of Robot Assisted Hand Stroke Rehabilitation System , 2009, 2009 International Conference on Computer and Automation Engineering.

[15]  S. Micera,et al.  Robotic techniques for upper limb evaluation and rehabilitation of stroke patients , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  C. DaSalla,et al.  Robot control using electromyography EMG signals of the wrist , 2005 .

[17]  Maurilio Marcacci,et al.  A NEW METHOD FOR ANTHROPOMETRIC ACQUISITION OF THE UPPER EXTREMITY PARAMETERS IN ELITE MASTER SWIMMERS , 2006 .