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A dynamic decision network framework for online media adaptation in stroke rehabilitation

Published:30 October 2008Publication History
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Abstract

In this article, we present a media adaptation framework for an immersive biofeedback system for stroke patient rehabilitation. In our biofeedback system, media adaptation refers to changes in audio/visual feedback as well as changes in physical environment. Effective media adaptation frameworks help patients recover generative plans for arm movement with potential for significantly shortened therapeutic time. The media adaptation problem has significant challenges—(a) high dimensionality of adaptation parameter space; (b) variability in the patient performance across and within sessions; (c) the actual rehabilitation plan is typically a non-first-order Markov process, making the learning task hard.

Our key insight is to understand media adaptation as a real-time feedback control problem. We use a mixture-of-experts based Dynamic Decision Network (DDN) for online media adaptation. We train DDN mixtures per patient, per session. The mixture models address two basic questions—(a) given a specific adaptation suggested by the domain experts, predict the patient performance, and (b) given the expected performance, determine the optimal adaptation decision. The questions are answered through an optimality criterion based search on DDN models trained in previous sessions. We have also developed new validation metrics and have very good results for both questions on actual stroke rehabilitation data.

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            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 5, Issue 1
            October 2008
            201 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/1404880
            Issue’s Table of Contents

            Copyright © 2008 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 30 October 2008
            • Revised: 1 June 2008
            • Accepted: 1 June 2008
            • Received: 1 February 2008
            Published in tomm Volume 5, Issue 1

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