A Novel Task-Specific Upper-Extremity Rehabilitation System with Interactive Game-Based Interface for Stroke Patients

Veena Jayasree-Krishnan, Dhruv Gamdha, Brian S. Goldberg, Shramana Ghosh, Preeti Raghavan, Vikram Kapila

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We present a novel task-specific upper-extremity rehabilitation system that uses an instrumented cup and an interactive gaming environment to promote patient engagement during repetitive rehabilitative exercise. The designed system tracks the movement of the cup as a stroke patient uses her forearm to perform a complex goal-oriented and task-specific activity, namely, grasping, lifting, and tilting the cup to drink from it. A force sensitive resistive sensor is mounted on the cup to constantly monitor the grasp force and the cup is endowed with rich features allowing a webcam to track and estimate its location and pose with the aid of state-of-the-art machine learning algorithms. This bi-manual forearm rehabilitation system is designed to enable stroke patients to perform an activity of daily living, namely, grasping, lifting, and tilting, repetitively, at their home as an exercise, so that they can relearn the motions of arm, wrist, and hand related to drinking.

Original languageEnglish
Title of host publication2019 International Symposium on Medical Robotics, ISMR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538678251
DOIs
StatePublished - May 8 2019
Externally publishedYes
Event2019 International Symposium on Medical Robotics, ISMR 2019 - Atlanta, United States
Duration: Apr 3 2019Apr 5 2019

Publication series

Name2019 International Symposium on Medical Robotics, ISMR 2019

Conference

Conference2019 International Symposium on Medical Robotics, ISMR 2019
Country/TerritoryUnited States
CityAtlanta
Period04/3/1904/5/19

Funding

This work is supported in part by the National Science Foundation grants DRK-12 DRL: 1417769, ITEST DRL: 1614085, and RET Site EEC: 1542286; NY Space Grant Consortium grant 76156-10488; and National Institute of Health grant R01HD071978.

FundersFunder number
National Institute of HealthR01HD071978
National Science Foundation76156-10488, 1614085, 1542286, DRK-12 DRL: 1417769
National Science Foundation1417769

    Keywords

    • Stroke
    • machine vision
    • pose estimation
    • rehabilitation
    • virtual environment

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