A learning-based agent for home neurorehabilitation.

Conference Proceeding

Abstract

  • This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies. The proposed system uses exercise-related motion information and electromyography signals (EMG) of a patient to train a Markov Decision Process (MDP). The trained MDP model can enable an agent to serve as a coach for a patient. On a system level, this is the first initiative, to the best of our knowledge, to employ LfD in an health-care application to enable lay users to program an intelligent system. From a rehabilitation research perspective, this is a completely novel initiative to employ machine learning to provide interactive corrective feedback to a patient in home settings.
  • Authors

  • Lydakis, Andreas
  • Meng, Yuanliang
  • Munroe, Christopher
  • Wu, Yi-Ning
  • Begum, Momotaz
  • Status

    Publication Date

  • July 2017
  • Keywords

  • Adolescent
  • Adult
  • Algorithms
  • Artificial Intelligence
  • Electromyography
  • Exercise Therapy
  • Feedback
  • Female
  • Humans
  • Neurological Rehabilitation
  • Robotics
  • Virtual Reality
  • Young Adult
  • Digital Object Identifier (doi)

    Pubmed Id

  • 28813990
  • Start Page

  • 1233
  • End Page

  • 1238
  • Volume

  • 2017