Article

Media adaptation framework in biofeedback system for stroke patient rehabilitation

Authors:

Sheng-Min LiuAuthors Info & Claims

MM '07: Proceedings of the 15th ACM international conference on Multimedia

Pages 47 - 57

https://doi.org/10.1145/1291233.1291248

Published: 29 September 2007 Publication History

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Abstract

In this paper, 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 expert, predict patient performance and (b) given an expected performance, determine 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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References

[1]

Y. CHEN, H. HUANG, W. XU, et al. (2006). The Design Of A Real-Time, Multimodal Biofeedback System For Stroke Patient Rehabilitation, SIG ACM Multimedia, Santa Barbara, CA, Oct. 2006.

Digital Library

Google Scholar

[2]

A. DEMPSTER, N. LAIRD and D. RUBIN (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39(1): 1--38.

Crossref

Google Scholar

[3]

R. O. DUDA, P. E. HART and D. G. STORK (2000). Pattern Classification, Wiley.

Digital Library

Google Scholar

[4]

D. J. GROUT and C. V. PALISCA (2001). A history of western music. New York, Norton: xvi, 843, 816 of plates.

Google Scholar

[5]

M. HOLDEN, E. TODOROV, J. CALLAHAN, et al. (1999). Virtual environment training improves motor performance in two patients with stroke: case report. Neurology Report. 23: 57--67.

Crossref

Google Scholar

[6]

P. J. HUBER (1981). Robust Statistics, Wiley.

Google Scholar

[7]

D. JACK, R. BOIAN, A. S. MERIANS, et al. (2001). Virtual reality-enhanced stroke rehabilitation. IEEE Trans. Neural Syst Rehabil Eng 9: 308--318.

Crossref

Google Scholar

[8]

L. P. KAELBLING, M. L. LITTMAN and A. R. CASSANDRA (1998). Planning and acting in partiallly observable stochastic domains. Artificial Intelligence 101: 99--134.

Digital Library

Google Scholar

[9]

K. MURPHY, A. TORRALBA and W. FREEMAN (2003). Using the Forest to See the Trees: A Graphical Model Relating Features, Objects and Scenes. Proc. NIPS'03 (Neural Info. Processing Systems). Whistler, Canada.

Google Scholar

[10]

S. RUSSELL and P. NORVIG (2003). Artificial Intelligence, A Modern Approach, Pearson Education, Inc.

Digital Library

Google Scholar

[11]

G. THEOCHAROUS, K. MURPHY and L. P. KAELBLING (2004). Representing hierarchical POMDPs as DBNs for multi-scale robot localization, International Conference on Robotics and Automation, 2004.

Crossref

Google Scholar

[12]

L. XIE, S.-F. CHANG, A. DIVAKARAN, et al. (2003). Unsupervised Discovery of Multilevel Statistical Video Structures Using Hierarchical Hidden Markov Models. Proc. IEEE Conference on Multimedia and Expo 2003. Baltomore MD, USA.

Digital Library

Google Scholar

[13]

W. XU, Y. CHEN, H. SUNDARAM, et al. (2006). Multimodal archiving, real-time collaborative annotation and information visualization in a biofeedback system for stroke patient rehabilitation, 3rd Workshop on Capture Archival, Retrieval of Personal Experiences, in Conjunction with ACMMM 06, Santa Barbara, CA, Oct. 2006.

Digital Library

Google Scholar

Cited By

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Ahmed TRikakis TKelliher AWolf S(2024)A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke RehabilitationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.345000832(3157-3166)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3450008
Sarker AEmenonye DKelliher ARikakis TBuehrer RAsbeck A(2022)Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation ApplicationsSensors10.3390/s2206230022:6(2300)Online publication date: 16-Mar-2022
https://doi.org/10.3390/s22062300
Morales ACibrian FCastro LTentori M(2021)An adaptive model to support biofeedback in AmI environments: a case study in breathing training for autismPersonal and Ubiquitous Computing10.1007/s00779-020-01512-126:6(1445-1460)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1007/s00779-020-01512-1
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Media adaptation framework in biofeedback system for stroke patient rehabilitation

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Information

Published In

MM '07: Proceedings of the 15th ACM international conference on Multimedia

September 2007

1115 pages

ISBN:9781595937025

DOI:10.1145/1291233

General Chairs:
Rainer Lienhart
University of Augsburg, Germany
,
Anand R. Prasad
DoCoMo Euro-Labs,Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Sunghyun Choi
Seoul National University, South Korea
,
Brian Bailey
University of Illinois at Urbana-Champaign
,
Nicu Sebe
University of Amsterdam, The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 September 2007

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MM07: The 15th ACM International Conference on Multimedia 2007

September 25 - 29, 2007

Augsburg, Germany

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Ahmed TRikakis TKelliher AWolf S(2024)A Hierarchical Bayesian Model for Cyber-Human Assessment of Movement in Upper Extremity Stroke RehabilitationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.345000832(3157-3166)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3450008
Sarker AEmenonye DKelliher ARikakis TBuehrer RAsbeck A(2022)Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation ApplicationsSensors10.3390/s2206230022:6(2300)Online publication date: 16-Mar-2022
https://doi.org/10.3390/s22062300
Morales ACibrian FCastro LTentori M(2021)An adaptive model to support biofeedback in AmI environments: a case study in breathing training for autismPersonal and Ubiquitous Computing10.1007/s00779-020-01512-126:6(1445-1460)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1007/s00779-020-01512-1
Rikakis TKelliher AChoi JHuang JKitani KZilevu SWolf S(2018)Semi-automated home-based therapy for the upper extremity of stroke survivorsProceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference10.1145/3197768.3197777(249-256)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3197768.3197777
Karime AEid MHaiwei Dong Halimi MGueaieb WEl Saddik A(2015)CAHR: A Contextually Adaptive Home-Based Rehabilitation FrameworkIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2014.234243264:2(427-438)Online publication date: Feb-2015
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Ko JChen WYu MLin HLin JWu SChung YHu IPeng WLin SChang CChou PChang KChang MChen SChen JLee MChen MHung Ydel Bimbo AChang SSmeulders A(2010)i-m-SpaceProceedings of the 18th ACM international conference on Multimedia10.1145/1873951.1874036(623-626)Online publication date: 25-Oct-2010
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Duff MYinpeng Chen Attygalle SHerman JSundaram HGang Qian Jiping He Rikakis T(2010)An Adaptive Mixed Reality Training System for Stroke RehabilitationIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2010.205506118:5(531-541)Online publication date: Oct-2010
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Swaminathan DThornburg HMumford JRajko SJames JIngalls TCampana EQian GSampath PPeng B(2009)A dynamic Bayesian approach to computational Laban shape quality analysisAdvances in Human-Computer Interaction10.1155/2009/3626512009(1-17)Online publication date: 1-Jan-2009
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Chen YXu WSundaram HRikakis TLiu S(2008)A dynamic decision network framework for online media adaptation in stroke rehabilitationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/1404880.14048845:1(1-38)Online publication date: 30-Oct-2008
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Abstract

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

Evaluating rehabilitation exercise performance using a single inertial measurement unit

The impact of vibrotactile biofeedback on the excessive walking sway and the postural control in elderly

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