Authors: Min Hun Lee 1 ; Daniel Siewiorek 1 ; Asim Smailagic 1 ; Alexandre Bernardino 2 and Sergi Bermúdez I Badia 3

Affiliations: 1 Carnegie Mellon University, United States ; 2 Instituto Superior Técnico, Portugal ; 3 Madeira Interactive Technology Institute and Faculdade de Ciências Exatas e da Engenharia and Universidade da Madeira, Portugal

Keyword(s): Stroke Rehabilitation, Artificial Intelligence, Exercise Recognition & Analysis.

Abstract: Therapists monitor and evaluate stroke patient’s motor abilities with clinical tests to individualize clinical interventions. After a clinical session, a therapist designs task-oriented exercises for a patient and requests self-reporting of patient’s adherence on exercise regimen. However, outpatients, who cannot receive feedback, often show low adherence [1], leading to sparse self-reports. It is difficult for therapists to follow patient’s progress. To address this challenge, this paper describes a Kinect-based monitoring system that evaluates performance and provides real-time feedback for four stroke rehabilitation exercises. Our preliminary study showed that this monitoring system can accurately monitor in-home stroke rehabilitation exercises.

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Paper citation in several formats:
Lee, M.; Siewiorek, D.; Smailagic, A.; Bernardino, A. and Bermúdez i Badia, S. (2017). A Kinect-based Monitoring System for Stroke Rehabilitation.In IcSPORTS 2017 - Extended Abstracts - AHA, ISBN , pages 8-10

author={Min Hun Lee. and Daniel Siewiorek. and Asim Smailagic. and Alexandre Bernardino. and Bermúdez i Badia, S.},
title={A Kinect-based Monitoring System for Stroke Rehabilitation},
booktitle={IcSPORTS 2017 - Extended Abstracts - AHA,},


JO - IcSPORTS 2017 - Extended Abstracts - AHA,
TI - A Kinect-based Monitoring System for Stroke Rehabilitation
SN -
AU - Lee, M.
AU - Siewiorek, D.
AU - Smailagic, A.
AU - Bernardino, A.
AU - Bermúdez i Badia, S.
PY - 2017
SP - 8
EP - 10
DO -

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