‘Feel the Painting’: a clinician-friendly approach to programming planar force fields for haptic devices

Haptic force fields are widely used in studies on motor adaptation, motor retention, and motor recovery in both healthy and impaired subjects. In the main paradigm the hand is guided or perturbed along specific paths or channels in order to investigate different aspects underlying the human motor control. Programming such fields for complex haptic environments can be very challenging and is often not feasible for clinicians and therapists. The aim of this paper is to introduce a more intuitive and clinician-friendly programming method capable of transforming a 2D drawing (stored as an image) into a haptic environment or planar force field. By considering the image intensity as a position-dependent potential field, the energy function is approximated through locally weighted projection regression (LWPR). Robot forces are then computed through the gradient of the regressed potential. The proposed method is validated with a two degrees-of-freedom planar manipulandum, the H-Man, and a preliminary shape recognition experiment involving blindfolded healthy subjects.

[1]  Manivannan Muniyandi,et al.  Tangible Images: Runtime Generation of Haptic Textures From Images , 2008, 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

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

[3]  J. Patton,et al.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors , 2005, Experimental Brain Research.

[4]  Massimo Bergamasco,et al.  Haptic desktop for assisted handwriting and drawing , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[5]  Stefan Schaal,et al.  A Library for Locally Weighted Projection Regression , 2008, J. Mach. Learn. Res..

[6]  P. Viviani,et al.  The law relating the kinematic and figural aspects of drawing movements. , 1983, Acta psychologica.

[7]  Michelle J. Johnson,et al.  Advances in upper limb stroke rehabilitation: a technology push , 2011, Medical & Biological Engineering & Computing.

[8]  R A Scheidt,et al.  Persistence of motor adaptation during constrained, multi-joint, arm movements. , 2000, Journal of neurophysiology.

[9]  S. Micera,et al.  Effects of the Alternate Combination of “Error-Enhancing” and “Active Assistive” Robot-Mediated Treatments on Stroke Patients , 2013, IEEE Journal of Translational Engineering in Health and Medicine.

[10]  Domenico Campolo,et al.  Upper Extremity Proprioception in Healthy Aging and Stroke Populations, and the Effects of Therapist- and Robot-Based Rehabilitation Therapies on Proprioceptive Function , 2015, Front. Hum. Neurosci..

[11]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[12]  Stefan Schaal,et al.  Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space , 2000, ICML.

[13]  Sethu Vijayakumar,et al.  Adaptive Optimal Feedback Control with Learned Internal Dynamics Models , 2010, From Motor Learning to Interaction Learning in Robots.

[14]  F A Mussa-Ivaldi,et al.  Adaptive representation of dynamics during learning of a motor task , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[15]  Etienne Burdet,et al.  A robotic teacher of Chinese handwriting , 2002, Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002.

[16]  E. Burdet,et al.  Motor adaptation with passive machines: A first study on the effect of real and virtual stiffness , 2014, Comput. Methods Programs Biomed..

[17]  P. Morasso.  Spatial control of arm movements , 2004, Experimental Brain Research.

[18]  Paolo Tommasino,et al.  H-Man: A planar, H-shape cabled differential robotic manipulandum for experiments on human motor control , 2014, Journal of Neuroscience Methods.

[19]  M. D. R. Minsky,et al.  Computational Haptics : The Sandpaper System for Synthesizing Texture for with a Force-Feedback Haptic Display , 1995 .

[20]  Domenico Campolo,et al.  Effects of Impedance Reduction of a Robot for Wrist Rehabilitation on Human Motor Strategies in Healthy Subjects during Pointing Tasks , 2011, Adv. Robotics.

[21]  Susan J. Lederman,et al.  Computational haptics: the sandpaper system for synthesizing texture for a force-feedback display , 1995 .

[22]  S. Micera,et al.  On the use of divergent force fields in robot-mediated neurorehabilitation , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[23]  Paolo Bonato,et al.  Haptic system for hand rehabilitation integrating an interactive game with an advanced robotic device , 2010, 2010 IEEE Haptics Symposium.