MODIFIED INTERNAL MODEL CONTROL FOR A THERAPEUTIC ROBOT

We present the use of the modified internal model controller (MIMC) and the “Probability Tube” (PT) action representation for robot-assisted upper extremities training of hemiplegic patients. The robot-assisted training session has two phases. During the first "demonstration" phase the robot learns from the therapist the target path through examples. In the second "exercise" phase the robot assists a patient to follow the target path. During this process, the control limits the interface force between the robot and the hand to be below the preset threshold (F = 50 N). The system allows the assessment of the range of movement, the positional error between the target and the reached position, the amount of added assistance (the interface force between the hand and the robot). We demonstrate the operation in two hemiplegic patients. The patients and therapist suggested after the tests that the new system is straightforward and intuitive for clinical applications.

[1]  G. Sandini,et al.  Robot therapy of the upper limb in stroke patients: preliminary experiences for the principle-based use of this technology. , 2009, Functional neurology.

[2]  P. Morasso,et al.  Braccio di Ferro: a new haptic workstation for neuromotor rehabilitation. , 2006, Technology and health care : official journal of the European Society for Engineering and Medicine.

[3]  Dejan B. Popovic,et al.  A method for assessing the arm movement performance: probability tube , 2013, Medical & Biological Engineering & Computing.

[4]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[5]  Miroslav R. Mataušek,et al.  Modified internal model control approach to the design and tuning of linear digital controllers , 2002, Int. J. Syst. Sci..

[6]  Gert Kwakkel,et al.  Intensity of practice after stroke: More is better , 2009 .

[7]  Robert Riener,et al.  ARMin: a robot for patient-cooperative arm therapy , 2007, Medical & Biological Engineering & Computing.

[8]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[9]  Dušan M. Stipanović,et al.  Modified nonlinear internal model control , 1998 .

[10]  William S. Harwin,et al.  Upper Limb Robot Mediated Stroke Therapy—GENTLE/s Approach , 2003, Auton. Robots.

[11]  David J. Reinkensmeyer,et al.  Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury , 2008, IEEE Transactions on Biomedical Engineering.

[12]  M. R. Matau.  Nonlinear Multi-Input-Multi-Output Neural Network Control of DC Motor Drive with Field Weakening , 1998 .

[13]  D. Popović,et al.  The Robot that Learns from the Therapist How to Assist Stroke Patients , 2014 .

[14]  J. Cozens.  Robotic assistance of an active upper limb exercise in neurologically impaired patients. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  R. Riener,et al.  ARMin - design of a novel arm rehabilitation robot , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[16]  Hermano Igo Krebs,et al.  MIT-MANUS: a workstation for manual therapy and training. I , 1992, [1992] Proceedings IEEE International Workshop on Robot and Human Communication.

[17]  Hermano Igo Krebs,et al.  Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.