Evaluation of negative viscosity as upper extremity training for stroke survivors

With stroke survivors (n=30) as the test population, we investigated how upper extremity training with negative viscosity affects coordination in unassisted conditions. Using a planar force-feedback device, subjects performed exploratory movements within an environment that simulated 1) negative viscosity added to elbow and shoulder joints 2) augmented inertia to the upper and lower arm combined with negative viscosity, or 3) a null force field (control). After training, we evaluated each subject's ability to perform circular movements in the null field. Negative viscosity training resulted in greater within-day reductions in error compared with the combined field training. Negative viscosity promoted greater distributions of accelerations during free exploration, especially in the sagittal axis, while combined field training diminished overall activity. Both force field training groups exhibited next day retention, while this was not observed for the control group. The improvement in performance suggests that greater range of kinematic experiences contribute to learning, even despite novel force field environments. These findings provide support for the use of movement amplifying environments for upper extremity rehabilitation, allowing greater access to training while maintaining user engagement.

[1]  Homayoon Kazerooni,et al.  The Human Power Amplifier Technology at the University Of California, Berkeley , 1996 .

[2]  J. Krakauer,et al.  Generalization of Motor Learning Depends on the History of Prior Action , 2006, PLoS biology.

[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]  Ferdinando A. Mussa-Ivaldi,et al.  Robot-assisted adaptive training: custom force fields for teaching movement patterns , 2004, IEEE Transactions on Biomedical Engineering.

[5]  Daniel M Wolpert,et al.  Kinematics and Dynamics Are Not Represented Independently in Motor Working Memory: Evidence from an Interference Study , 2002, The Journal of Neuroscience.

[6]  M. Arbib,et al.  Infant grasp learning: a computational model , 2004, Experimental Brain Research.

[7]  C. Braun,et al.  Motor learning elicited by voluntary drive. , 2003, Brain : a journal of neurology.

[8]  Zoubin Ghahramani,et al.  Modular decomposition in visuomotor learning , 1997, Nature.

[9]  Olivier White,et al.  Use-Dependent and Error-Based Learning of Motor Behaviors , 2010, The Journal of Neuroscience.

[10]  R. Hanlon Motor learning following unilateral stroke. , 1996, Archives of physical medicine and rehabilitation.

[11]  Felix C Huang,et al.  Manual skill generalization enhanced by negative viscosity. , 2010, Journal of neurophysiology.

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

[13]  M.A. Peshkin,et al.  Active-Impedance Control of a Lower-Limb Assistive Exoskeleton , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

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

[15]  S. Schaal,et al.  One-Handed Juggling: A Dynamical Approach to a Rhythmic Movement Task. , 1996, Journal of motor behavior.

[16]  Stephan Riek,et al.  The interference effects of non-rotated versus counter-rotated trials in visuomotor adaptation , 2007, Experimental Brain Research.

[17]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[18]  M.J. Johnson,et al.  Experimental results using force-feedback cueing in robot-assisted stroke therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[20]  W. Rymer,et al.  Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? , 2006, Journal of rehabilitation research and development.

[21]  N. Hogan,et al.  Customized interactive robotic treatment for stroke: EMG-triggered therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[23]  Michael I. Jordan,et al.  The Role of Inertial Sensitivity in Motor Planning , 1998, The Journal of Neuroscience.

[24]  E Bizzi,et al.  Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a Difficult Motor Task. , 1997, Journal of motor behavior.

[25]  Tal Jarus,et al.  Effects of Cognitive Processes and Task Complexity on Acquisition, Retention, and Transfer of Motor Skills , 2001 .

[26]  David J Reinkensmeyer,et al.  Manually‐Assisted Versus Robotic‐Assisted Body Weight−Supported Treadmill Training in Spinal Cord Injury: What Is the Role of Each? , 2010, PM & R : the journal of injury, function, and rehabilitation.

[27]  D.J. Reinkensmeyer,et al.  Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  E. Thelen,et al.  The transition to reaching: mapping intention and intrinsic dynamics. , 1993, Child development.

[29]  W. Rymer,et al.  Adaptive assistance for guided force training in chronic stroke , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  F. Mussa-Ivaldi,et al.  The motor system does not learn the dynamics of the arm by rote memorization of past experience. , 1997, Journal of neurophysiology.

[31]  Ferdinando A. Mussa-Ivaldi,et al.  Sequence, time, or state representation: how does the motor control system adapt to variable environments? , 2003, Biological Cybernetics.

[32]  Daniel M. Wolpert,et al.  Internal models underlying grasp can be additively combined , 2004, Experimental Brain Research.