TY - GEN
T1 - A Novel Task-Specific Upper-Extremity Rehabilitation System with Interactive Game-Based Interface for Stroke Patients
AU - Jayasree-Krishnan, Veena
AU - Gamdha, Dhruv
AU - Goldberg, Brian S.
AU - Ghosh, Shramana
AU - Raghavan, Preeti
AU - Kapila, Vikram
N1 - Funding Information:
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.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/8
Y1 - 2019/5/8
N2 - 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.
AB - 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.
KW - Stroke
KW - machine vision
KW - pose estimation
KW - rehabilitation
KW - virtual environment
UR - http://www.scopus.com/inward/record.url?scp=85066340352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066340352&partnerID=8YFLogxK
U2 - 10.1109/ISMR.2019.8710184
DO - 10.1109/ISMR.2019.8710184
M3 - Conference contribution
AN - SCOPUS:85066340352
T3 - 2019 International Symposium on Medical Robotics, ISMR 2019
BT - 2019 International Symposium on Medical Robotics, ISMR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Symposium on Medical Robotics, ISMR 2019
Y2 - 3 April 2019 through 5 April 2019
ER -