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A Computational Study of Robotic Therapy for Stroke Rehabilitation Based on Population Coding

  • Yuki Ueyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)

Abstract

We evaluated the efficiency of robotic therapy for stroke survivors by using a computational approach in motor theory with a stroke rehabilitation model. In computational neuroscience, hand movement can be represented by population coding of neuronal preferred directions (PDs) in the motor cortex. We modeled the recovery processes of arm movement in conventional and robotic therapies as reoptimization of PDs in different learning rules, and compared the efficiencies after stroke. Conventional therapy did not induce complete recovery of stroke lesions, and the neuronal state depended on the training direction. However, robotic therapy reoptimized the PDs uniformly regardless of the training direction. These observations suggest that robotic therapy may be effective for recovery and not have a negative effect on motor performance depending the training direction. Furthermore, this study provides computational evidence to promote robotic therapy for stroke rehabilitation.

Keywords

Motor learning Motor cortex Preferred direction Reinforcement learning Reaching 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yuki Ueyama
    • 1
  1. 1.Department of Rehabilitation EngineeringResearch Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawaJapan

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