A Computational-Model-Based Study of Supervised Haptics-Enabled Therapist-in-the-Loop Training for Upper-Limb Poststroke Robotic Rehabilitation

Seyed Farokh Atashzar, Mahya Shahbazi, Mahdi Tavakoli, Rajni V. Patel

Research output: Contribution to journalArticle

Abstract

This paper proposes a new framework for neural-network-based supervised training of intensity and strategy for upper-limb haptics-enabled robotic neurorehabilitation systems for poststroke motor disabilities. Two alternative approaches are implemented: 1) Haptics-enabled Teleoperated Supervised Training (HTST); and 2) Electromyography-based Indirect Supervised Training (EIST). The design of both techniques includes two phases: 1) characterizing and learning the therapeutic intensity and strategy when a therapist delivers robotics-assisted rehabilitation to a patient (demonstration phase); and 2) enabling regeneration of the learned therapeutic behavior when the therapist is out of the loop, e.g., when she/he is working with another patient (regeneration phase). For the first phase, the HTST platform allows for direct transformation of the forces generated by the therapist to deliver rehabilitation at the patient side, and providing the therapist with direct force feedback. In contrast, EIST is an indirect platform that utilizes the posture of the therapist for generation of rehabilitation forces. EIST uses vibration to the therapist's arm to make the therapist aware of the forces applied to the patient's hand. Although HTST is a more intuitive alternative, EIST is safer, portable, wearable, less expensive, and provides relative motion freedom for the therapist. The proposed training framework is motivated by the existing challenge regarding the need for tuning the strategy and intensity of robotic rehabilitation systems in a patient-specific manner. It also enables therapists to share their time between several patients. Experimental results are presented to evaluate the engineering aspects of the work and feasibility of the concept, where a computational model is used to simulate motor disability of a poststroke patient.

Original languageEnglish (US)
Pages (from-to)563-574
Number of pages12
JournalIEEE/ASME Transactions on Mechatronics
Volume23
Issue number2
DOIs
StatePublished - Apr 2018

Keywords

  • Bio-signal processing
  • haptics
  • learning from demonstration
  • machine learning
  • neural networks
  • rehabilitation robotics
  • telerobotic rehabilitation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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