A Method to Study Precision Grip Control in Viscoelastic Force Fields Using a Robotic Gripper

Instrumented objects and multipurpose haptic displays have commonly been used to investigate sensorimotor control of grasping and manipulation. A major limitation of these devices, however, is the extent to which the experimenter can vary the interaction dynamics to fully probe sensorimotor control mechanisms. We propose a novel method to study precision grip control using a grounded robotic gripper with two moving, mechanically coupled finger pads instrumented with force sensors. The device is capable of stably rendering virtual mechanical properties with a wide dynamic range of achievable impedances. Eight viscoelastic force fields with different combinations of stiffness and damping parameters were implemented, and tested on eight healthy subjects performing 30 consecutive repetitions of a grasp, hold, and release task with time and position constraints. Rates of thumb and finger force were found to be highly correlated (r>0.9) during grasping, revealing that, despite the mechanical coupling of the two finger pads, subjects performed grasping movements in a physiological fashion. Subjects quickly adapted to the virtual dynamics (within seven trials), but, depending on the presented force field condition, used different control strategies to correctly perform the task. The proof of principle presented in this paper underscores the potential of such a one-degree-of-freedom robotic gripper to study neural control of grasping, and to provide novel insights on sensorimotor control mechanisms.

[1]  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.

[2]  S C Gandevia,et al.  Thumb and finger forces produced by motor units in the long flexor of the human thumb , 2007, The Journal of physiology.

[3]  T. Milner,et al.  A model for the generation of movements requiring endpoint precision , 1992, Neuroscience.

[4]  Vladimir M. Zatsiorsky,et al.  Prehension synergies during smooth changes of the external torque , 2011, Experimental Brain Research.

[5]  Joachim Hermsdörfer,et al.  Evaluation of precision grip using pneumatically controlled loads , 1992, Journal of Neuroscience Methods.

[6]  J. F. Soechting,et al.  Force synergies for multifingered grasping , 2000, Experimental Brain Research.

[7]  C. L. Van Doren,et al.  Differential effects of load stiffness on matching pinch force, finger span, and effort , 1998, Experimental Brain Research.

[8]  Marco Santello,et al.  Context-Dependent Learning Interferes with Visuomotor Transformations for Manipulation Planning , 2012, The Journal of Neuroscience.

[9]  R. Johansson,et al.  Somatosensory control of precision grip during unpredictable pulling loads , 2004, Experimental Brain Research.

[10]  R. Howe,et al.  Identification of the mechanical impedance at the human finger tip. , 1997, Journal of biomechanical engineering.

[11]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

[12]  R. S. Johansson,et al.  Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects , 2004, Experimental Brain Research.

[13]  Luis F. Schettino,et al.  Effects of object shape and visual feedback on hand configuration during grasping , 2003, Experimental Brain Research.

[14]  Fan Gao,et al.  Prehension stability: experiments with expanding and contracting handle. , 2006, Journal of neurophysiology.

[15]  Francisco J Valero-Cuevas,et al.  Neural Control of Motion-to-Force Transitions with the Fingertip , 2008, The Journal of Neuroscience.

[16]  R. Johansson,et al.  Visual size cues in the programming of manipulative forces during precision grip , 2004, Experimental Brain Research.

[17]  R. Johansson,et al.  Coordinated isometric muscle commands adequately and erroneously programmed for the weight during lifting task with precision grip , 2004, Experimental Brain Research.

[18]  M. A. Srinivassan.  The impact of visual information on the haptic perception of stiffness in virtual environments , 1996 .

[19]  Ryuichi Yokogawa,et al.  Manipulabilities of the index finger and thumb in three tip-pinch postures. , 2004, Journal of biomechanical engineering.

[20]  Michael Sjöström,et al.  Hand span influences optimal grip span in male and female teenagers. , 2006, The Journal of hand surgery.

[21]  Alexander S Aruin,et al.  Grip Force Control in Individuals With Multiple Sclerosis , 2009, Neurorehabilitation and neural repair.

[22]  Marco Santello,et al.  Effects of Carpal Tunnel Syndrome on Adaptation of Multi-Digit Forces to Object Weight for Whole-Hand Manipulation , 2011, PloS one.

[23]  K. J. Cole,et al.  Sensory-motor coordination during grasping and manipulative actions , 1992, Current Biology.

[24]  Joachim Hermsdörfer,et al.  Grip force behavior during object manipulation in neurological disorders: Toward an objective evaluation of manual performance deficits , 2005, Movement disorders : official journal of the Movement Disorder Society.

[25]  Martha Flanders,et al.  Effects of object compliance on three-digit grasping. , 2009, Journal of neurophysiology.

[26]  J R Flanagan,et al.  The Role of Internal Models in Motion Planning and Control: Evidence from Grip Force Adjustments during Movements of Hand-Held Loads , 1997, The Journal of Neuroscience.

[27]  R. Johansson,et al.  Factors influencing the force control during precision grip , 2004, Experimental Brain Research.

[28]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[29]  Martha Flanders,et al.  Somatosensory Comparison during Haptic Tracing , 2011, Cerebral cortex.

[30]  M A Srinivasan,et al.  Manual discrimination of compliance using active pinch grasp: The roles of force and work cues , 1995, Perception & psychophysics.

[31]  I Salimi,et al.  Specificity of internal representations underlying grasping. , 2000, Journal of neurophysiology.

[32]  K. J. Cole,et al.  Friction at the digit-object interface scales the sensorimotor transformation for grip responses to pulling loads , 2004, Experimental Brain Research.

[33]  Giulio Sandini,et al.  Finger Impedance Evaluation by Means of Hand Exoskeleton , 2011, Annals of Biomedical Engineering.

[34]  Mandayam A. Srinivasan,et al.  Manual resolution of viscosity and mass , 1995 .

[35]  F. Valero-Cuevas,et al.  The strength-dexterity test as a measure of dynamic pinch performance. , 2003, Journal of biomechanics.

[36]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[37]  Miles C. Bowman,et al.  Control strategies in object manipulation tasks , 2006, Current Opinion in Neurobiology.

[38]  Olivier Lambercy,et al.  High-fidelity rendering of virtual objects with the ReHapticKnob - novel avenues in robot-assisted rehabilitation of hand function , 2012, 2012 IEEE Haptics Symposium (HAPTICS).

[39]  Francisco J Valero-Cuevas,et al.  Manipulating the edge of instability. , 2007, Journal of biomechanics.

[40]  Tara L. McIsaac,et al.  Within-trial modulation of multi-digit forces to friction , 2011, Experimental Brain Research.

[41]  R G Dong,et al.  Distribution of mechanical impedance at the fingers and the palm of the human hand. , 2005, Journal of biomechanics.

[42]  Geoffrey P. Bingham,et al.  Intrinsic scaling of reaches-to-grasp predicted by affordance-based model: Testing men and women with large and small grip spans , 2011 .

[43]  M. Santello,et al.  Anticipatory Planning and Control of Grasp Positions and Forces for Dexterous Two-Digit Manipulation , 2010, The Journal of Neuroscience.