Learning, Retention, and Slacking: A Model of the Dynamics of Recovery in Robot Therapy

Quantitative descriptions of the process of recovery of motor functions in impaired subjects during robot-assisted exercise might help to understand how to use these devices to make recovery faster and more effective. Linear dynamical models have been used to describe the dynamics of sensorimotor adaptation. Here, we extend this formalism to characterize the neuromotor recovery process. We focus on a robot therapy experiment that involved chronic stroke survivors, based on a robot-assisted arm extension task. The results suggest that modeling the recovery process with dynamical models is feasible, and could allow predicting the long-term outcome of a robot-assisted rehabilitation treatment.

[1]  A. Fugl-Meyer,et al.  The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. , 1975, Scandinavian journal of rehabilitation medicine.

[2]  Richard W. Bohannon,et al.  Interrater reliability of a modified Ashworth scale of muscle spasticity. , 1987, Physical therapy.

[3]  J. Kleim,et al.  Functional reorganization of the rat motor cortex following motor skill learning. , 1998, Journal of neurophysiology.

[4]  C. Winstein,et al.  Motor learning after unilateral brain damage , 1999, Neuropsychologia.

[5]  N. Hogan,et al.  Robot training enhanced motor outcome in patients with stroke maintained over 3 years , 1999, Neurology.

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

[7]  R. Nudo,et al.  Effects of Repetitive Motor Training on Movement Representations in Adult Squirrel Monkeys: Role of Use versus Learning , 2000, Neurobiology of Learning and Memory.

[8]  J. Kleim,et al.  Sensitivity of cortical movement representations to motor experience: evidence that skill learning but not strength training induces cortical reorganization , 2001, Behavioural Brain Research.

[9]  N. Hogan,et al.  Robot-aided sensorimotor arm training improves outcome in patients with chronic stroke , 2003, Neurology.

[10]  Reza Shadmehr,et al.  Quantifying Generalization from Trial-by-Trial Behavior of Adaptive Systems that Learn with Basis Functions: Theory and Experiments in Human Motor Control , 2003, The Journal of Neuroscience.

[11]  J. Kleim,et al.  Cortical Synaptogenesis and Motor Map Reorganization Occur during Late, But Not Early, Phase of Motor Skill Learning , 2004, The Journal of Neuroscience.

[12]  Steven C Cramer,et al.  Robotics, motor learning, and neurologic recovery. , 2004, Annual review of biomedical engineering.

[13]  Jian-Hua Zhang,et al.  A nonlinear adaptive fuzzy approximator technique with its application to prediction of non-stationary EEG dynamics and estimation of single-sweep evoked potentials. , 2005, Technology and health care : official journal of the European Society for Engineering and Medicine.

[14]  R. Shadmehr,et al.  Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. , 2005, Journal of neurophysiology.

[15]  R. Shadmehr,et al.  Internal models and contextual cues: encoding serial order and direction of movement. , 2005, Journal of neurophysiology.

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

[17]  R. Nudo.  Mechanisms for recovery of motor function following cortical damage , 2006, Current Opinion in Neurobiology.

[18]  J. Krakauer.  Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[19]  J. Kleim,et al.  Motor training induces experience-specific patterns of plasticity across motor cortex and spinal cord. , 2006, Journal of applied physiology.

[20]  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.

[21]  Gert Kwakkel,et al.  Impact of intensity of practice after stroke: Issues for consideration , 2006 .

[22]  D. Reinkensmeyer,et al.  Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed , 2007, Journal of NeuroEngineering and Rehabilitation.

[23]  Maarten J. IJzerman,et al.  Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. , 2006, Journal of rehabilitation research and development.

[24]  Gert Kwakkel,et al.  Impact of Time on Improvement of Outcome After Stroke , 2006, Stroke.

[25]  Philip N. Sabes,et al.  Modeling Sensorimotor Learning with Linear Dynamical Systems , 2006, Neural Computation.

[26]  G. Kwakkel.  Impact of intensity of practice after stroke: issues for consideration. , 2012, Disability and rehabilitation.

[27]  R. Shadmehr,et al.  Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning , 2006, PLoS biology.

[28]  P. Langhorne,et al.  Repetitive Task Training for Improving Functional Ability After Stroke , 2009, The Cochrane database of systematic reviews.

[29]  Konrad Paul Kording,et al.  The dynamics of memory as a consequence of optimal adaptation to a changing body , 2007, Nature Neuroscience.

[30]  Raul Benitez,et al.  Motor adaptation as a greedy optimization of error and effort. , 2007, Journal of neurophysiology.

[31]  T. Platz,et al.  Electromechanical and robot-assisted arm training for improving arm function and activities of daily living after stroke. , 2008, The Cochrane database of systematic reviews.

[32]  Michael A. Arbib,et al.  Stroke Rehabilitation Reaches a Threshold , 2008, PLoS Comput. Biol..

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

[34]  Maura Casadio,et al.  Minimally assistive robot training for proprioception enhancement , 2009, Experimental Brain Research.

[35]  Maura Casadio,et al.  Abnormal sensorimotor control, but intact force field adaptation, in multiple sclerosis subjects with no clinical disability , 2008, Multiple sclerosis.

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

[37]  N. Schweighofer,et al.  Dual Adaptation Supports a Parallel Architecture of Motor Memory , 2009, The Journal of Neuroscience.

[38]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2009, Circulation.

[39]  Vincent S. Huang,et al.  Robotic neurorehabilitation: a computational motor learning perspective , 2009, Journal of NeuroEngineering and Rehabilitation.

[40]  P. Morasso,et al.  Self-adaptive robot training of stroke survivors for continuous tracking movements , 2010, Journal of NeuroEngineering and Rehabilitation.

[41]  P. Heuschmann,et al.  Incidence of stroke in Europe at the beginning of the 21st century. , 2009, Stroke.

[42]  Nicolas Schweighofer,et al.  A Functional Threshold for Long-Term Use of Hand and Arm Function Can Be Determined: Predictions From a Computational Model and Supporting Data From the Extremity Constraint-Induced Therapy Evaluation (EXCITE) Trial , 2009, Physical Therapy.

[43]  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.

[44]  N. Hogan,et al.  Learning, Not Adaptation, Characterizes Stroke Motor Recovery: Evidence From Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.