Autonomously Adapting Robotic Assistance for Rehabilitation Therapy

The goal of our research is to develop a novel control framework to provide robotic assistance for rehabilitation of the hemiparetic upper extremity after stroke. The control framework is designed to provide an optimal time-varying assistive force to stroke patients in varying physical and environmental conditions. An artificial neural network (ANN) based proportional-integral (PI) gain scheduling direct force controller is designed to provide optimal force assistance in a precise and smooth manner. The human arm model is integrated within the control framework where ANN uses estimated human arm parameters to select the appropriate PI gains. Experimental results are presented to demonstrate the effectiveness and feasibility of the proposed control framework

[1]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[2]  D.J. Reinkensmeyer,et al.  Robotic movement training as an optimization problem: designing a controller that assists only as needed , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[3]  H. F. Machiel van der Loos,et al.  Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. , 2000, Journal of rehabilitation research and development.

[4]  Reza Shadmehr,et al.  The Equilibrium Point Hypothesis for Control of Movements , 1998 .

[5]  J. Forbes.  Cost of Stroke , 1993, Scottish medical journal.

[6]  N. Miller,et al.  Technique to improve chronic motor deficit after stroke. , 1993, Archives of physical medicine and rehabilitation.

[7]  Toru Tsumugiwa,et al.  Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[8]  David B Matchar,et al.  Cost-effectiveness of antiplatelet agents in secondary stroke prevention: the limits of certainty. , 2005, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[9]  E. Taub,et al.  Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation--a clinical review. , 1999, Journal of rehabilitation research and development.

[10]  D. Erol,et al.  A new control approach to robot assisted rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[11]  Eduardo Martínez-Vila,et al.  The Cost of Stroke , 2003, Cerebrovascular Diseases.

[12]  B. Volpe.  "Stroke, stroke": a coxswain's call for more work and more innovation. , 2004, Journal of rehabilitation research and development.

[13]  Ryojun Ikeura,et al.  Control characteristics of two humans in cooperative task , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[14]  W. Rymer,et al.  Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. , 2014, Journal of rehabilitation research and development.

[15]  N. Hogan,et al.  Robot-aided neurorehabilitation. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  Bruno Siciliano,et al.  Modeling and Control of Robot Manipulators , 1995 .