Media adaptation framework in biofeedback system for stroke patient rehabilitation

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.

REFERENCES

• 1: Yinpeng Chen , He Huang , Weiwei Xu , Richard Isaac Wallis , Hari Sundaram , Thanassis Rikakis , Todd Ingalls , Loren Olson , Jiping He, The design of a real-time, multimodal biofeedback system for stroke patient rehabilitation, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA [doi>10.1145/1180639.1180804]
• 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.
• 3: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 4: D. J. GROUT and C. V. PALISCA (2001). A history of western music. New York, Norton: xvi, 843, 816 of plates.
• 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.
• 6: P. J. HUBER (1981). Robust Statistics, Wiley.
• 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.
• 8: Leslie Pack Kaelbling , Michael L. Littman , Anthony R. Cassandra, Planning and acting in partially observable stochastic domains, Artificial Intelligence, v.101 n.1-2, p.99-134, May 1998 [doi>10.1016/S0004-3702(98)00023-X]
• 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.
• 10: Stuart J. Russell , Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson Education, 2003
• 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.
• 12: Lexing Xie , Shih-Fu Chang , A. Divakaran , Huifang Sun, Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models, Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03), p.29-32, July 06-09, 2003
• 13: Weiwei Xu , Yinpeng Chen , Hari Sundaram , Thanassis Rikakis, Multimodal archiving, real-time annotation and information visualization in a biofeedback system for stroke patient rehabilitation, Proceedings of the 3rd ACM workshop on Continuous archival and retrival of personal experences, October 28-28, 2006, Santa Barbara, California, USA [doi>10.1145/1178657.1178661]

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Health

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Computer-supported cooperative work

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
biofeedback, dynamic decision network, media adaptation, mixture of experts