Learning new movements after paralysis: Results from a home-based study

Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility. The use of the BMI over one month led to increased range of motion and force at the shoulders in chronic survivors. Concurrently, subjects learned to reorganize their body motions as they practiced the control of a computer cursor to perform different tasks and games. The BMI allowed subjects to generate any movement of the cursor with different motions of their body. Through practice subjects demonstrated a tendency to increase the similarity between the body motions used to control the cursor in distinct tasks. Nevertheless, by the end of learning, some significant and persistent differences appeared to persist. This suggests the ability of the central nervous system to concurrently learn operating the BMI while exploiting the possibility to adapt the available mobility to the specific spatio-temporal requirements of each task.

[1]  M. Casadio,et al.  Body machine interface: Remapping motor skills after spinal cord injury , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[2]  Paolo Bonato,et al.  Wearable Sensors and Systems , 2010, IEEE Engineering in Medicine and Biology Magazine.

[3]  R. Ryan,et al.  Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. , 1982 .

[4]  Rajiv Ranganathan,et al.  Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept , 2017, Neurorehabilitation and neural repair.

[5]  J. Foley The co-ordination and regulation of movements , 1968 .

[6]  Privender Saini,et al.  Philips stroke rehabilitation exerciser: a usability test , 2008 .

[7]  Rajiv Ranganathan,et al.  The Body-Machine Interface: A New Perspective on an Old Theme , 2012, Journal of motor behavior.

[8]  E. Todorov,et al.  Analysis of the synergies underlying complex hand manipulation , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  M. Latash,et al.  Motor Control Strategies Revealed in the Structure of Motor Variability , 2002, Exercise and sport sciences reviews.

[10]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[11]  N. Hogan,et al.  Quantization of continuous arm movements in humans with brain injury. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  F. Mussa-Ivaldi,et al.  Functional reorganization of upper-body movement after spinal cord injury , 2010, Experimental Brain Research.

[13]  Daniel A. Braun,et al.  Structure learning in action , 2010, Behavioural Brain Research.

[14]  Ferdinando A. Mussa-Ivaldi,et al.  Body machine interfaces for neuromotor rehabilitation: A case study , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  P. Wedin On angles between subspaces of a finite dimensional inner product space , 1983 .

[16]  Andrea Corradini,et al.  Handbook of Research on Improving Learning and Motivation through Educational Games , 2011 .

[17]  M. Latash,et al.  Optimality vs. variability: an example of multi-finger redundant tasks , 2010, Experimental Brain Research.

[18]  Elias B. Thorp,et al.  Remapping residual coordination for controlling assistive devices and recovering motor functions , 2015, Neuropsychologia.

[19]  Helen J. Huang,et al.  Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics , 2012, The Journal of Neuroscience.

[20]  Zachary Danziger,et al.  Learning Algorithms for Human–Machine Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

[21]  Marybeth Brown,et al.  Daniels and Worthingham's Muscle Testing : Techniques of Manual Examination and Performance Testing , 2013 .

[22]  William S. Harwin,et al.  Challenges and Opportunities for Robot-Mediated Neurorehabilitation , 2006, Proceedings of the IEEE.

[23]  Toni Giorgino,et al.  Wireless Support to Poststroke Rehabilitation: MyHeart's Neurological Rehabilitation Concept , 2009, IEEE Transactions on Information Technology in Biomedicine.

[24]  T. Poggio,et al.  Ill-posed problems in early vision: from computational theory to analogue networks , 1985, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[25]  Rajiv Ranganathan,et al.  Learning to be Lazy: Exploiting Redundancy in a Novel Task to Minimize Movement-Related Effort , 2013, The Journal of Neuroscience.

[26]  D. Reinkensmeyer,et al.  Technologies and combination therapies for enhancing movement training for people with a disability , 2012, Journal of NeuroEngineering and Rehabilitation.

[27]  Camilla Pierella,et al.  Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Toni Giorgino,et al.  Sensor Evaluation for Wearable Strain Gauges in Neurological Rehabilitation , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[30]  Maura Casadio,et al.  Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly learned sensorimotor transformation. , 2011, Journal of neurophysiology.

[31]  Louise Demers,et al.  Quebec User Evaluation of Satisfaction with Assistive Technology--Revised , 2017 .

[32]  Ferdinando A Mussa-Ivaldi,et al.  Remapping hand movements in a novel geometrical environment. , 2005, Journal of neurophysiology.

[33]  J. Pons,et al.  Emerging Therapies in Neurorehabilitation , 2013 .

[34]  Gene H. Golub,et al.  Numerical methods for computing angles between linear subspaces , 1971, Milestones in Matrix Computation.

[35]  Rafael Raya,et al.  Emerging Therapies in Neurorehabilitation II , 2015 .

[36]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[37]  Anna Åkerfeldt,et al.  Exploring educational video game design: meaning potentials and implications for learning , 2011 .

[38]  P. Tonali,et al.  Effects of rehabilitation on quality of life in patients with chronic stroke , 2008, Brain injury.

[39]  R Weiss-Lambrou,et al.  Item Analysis of the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) , 2000, Assistive technology : the official journal of RESNA.

[40]  Ferdinando A. Mussa-Ivaldi,et al.  A body machine interface based on inertial sensors , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  F. A. Mussa-Ivaldi,et al.  Reorganization of motor function and space representation in body machine interfaces , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[42]  Daniel A. Braun,et al.  Motor Task Variation Induces Structural Learning , 2009, Current Biology.