High-fidelity rendering of virtual objects with the ReHapticKnob - novel avenues in robot-assisted rehabilitation of hand function

Rehabilitation robots can provide intensive and motivating therapy after stroke in order to further promote recovery of sensorimotor function. To provide the necessary patient-robot interaction for the assessment and training of the hand throughout the different phases of recovery, high-fidelity haptic interfaces with a wide impedance width (Z-width) are required. In this paper the Z-width and haptic interaction quality of a 2 degree-of-freedom (DOF) end-effector based hand rehabilitation robot called the ReHapticKnob are evaluated and strategies to improve these parameters are investigated. An impedance-based controller with force feedback was implemented to modulate the apparent impedance of the robot's end-effector. Additionally, a discrete-time adaptive velocity estimator was used to increase the Z-width of the device. The resulting impedance is evaluated and compared to a commercial haptic device (Phantom Premium 1.5) and the achieved Z-width is analyzed in frequency space and on a K-B-plot. With the proposed control strategy the ReHapticKnob shows similar transparent behavior as a Phantom Premium 1.5 but can render much higher impedances, resulting in a unique high-fidelity patient-robot interaction capable of adapting to different impairments and presenting various haptic stimuli.

[1]  H. Woldag,et al.  Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients , 2002, Journal of Neurology.

[2]  Evren Samur Systematic evaluation methodology and performance metrics for haptic interfaces , 2011, WHC 2011.

[3]  J. Edward Colgate,et al.  Factors affecting the Z-Width of a haptic display , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[4]  Vincent Hayward,et al.  Discrete-time adaptive windowing for velocity estimation , 2000, IEEE Trans. Control. Syst. Technol..

[5]  R. Colombo,et al.  Measuring Changes of Movement Dynamics During Robot-Aided Neurorehabilitation of Stroke Patients , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Marko Munih,et al.  Upper limb motion analysis using haptic interface , 2001 .

[7]  Chee Leong Teo,et al.  A Haptic Knob for Rehabilitation of Hand Function , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Olivier Lambercy,et al.  Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Vincent Hayward,et al.  Performance Measures for Haptic Interfaces , 1996 .

[10]  R. Gassert,et al.  Upper limb assessment using a Virtual Peg Insertion Test , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[11]  Rahsaan J. Holley,et al.  Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot , 2010, Journal of NeuroEngineering and Rehabilitation.

[12]  Marcia Kilchenman O'Malley,et al.  Application of Levant's differentiator for velocity estimation and increased Z-width in haptic interfaces , 2011, 2011 IEEE World Haptics Conference.

[13]  S. K. Wee,et al.  Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study , 2011, Journal of NeuroEngineering and Rehabilitation.

[14]  H. Krebs,et al.  Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.

[15]  E. Burdet,et al.  Robot-assisted rehabilitation of hand function. , 2010, Current opinion in neurology.

[16]  Haruhisa Kawasaki,et al.  A hand rehabilitation support system with improvements based on clinical practices , 2009 .

[17]  Kevin Cleary,et al.  Closed-Loop Force Control for Haptic Simulation of Virtual Environments , 2000 .

[18]  K. Mauritz,et al.  Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand , 1995, Journal of the Neurological Sciences.

[19]  H. Freund,et al.  Invariant temporal characteristics of manipulative hand movements , 2004, Experimental Brain Research.