COGNITIVE STATE ESTIMATION FOR ADAPTIVE LEARNING SYSTEMS USING WEARABLE PHYSIOLOGICAL SENSORS

Aniket A. Vartak, Cali M. Fidopiastis, Denise M. Nicholson, Wasfy B. Mikhael, Dylan D. Schmorrow

Abstract

This paper presents a historical overview of intelligent tutoring systems and describes an adaptive instructional architecture based upon current instructional and adaptive design theories. The goal of such an endeavor is to create a training system that can dynamically change training content and presentation based upon an individual’s real-time measure of cognitive state changes. An array of physiological sensors is used to estimate the cognitive state of the learner. This estimate then drives the adaptive mitigation strategy, which is used as a feed-back and changes how the learning information is presented. The underlying assumptions are that real-time monitoring of the learners cognitive state and the subsequent adaptation of the system will maintain the learner in an overall state of optimal learning. The main issues concerning this approach are constructing cognitive state estimators from a multimodal array of physiological sensors and assessing initial baseline values, as well as changes in baseline. We discuss these issues in a data processing block wise structure, where the blocks include synchronization of different data streams, feature extraction, and forming a cognitive state metric by classification/clustering of the features. Initial results show our current capabilities of combining several data streams and determining baseline values. Given that this work is in its initial staged the work points to our ongoing research and future directions.

References

  1. Nicholson, D., Stanney, K., Fiore, S., Davis, L., Fidopiastis, C., Finkelstein, N., & Arnold, R. 2006, 'An adaptive system for improving and augmenting human performance', In D.D. Schmorrow, L.M. Reeves, and K.M. Stanney (eds.): Foundations of Augmented Cognition 2nd Edition, Arlington, VA: Strategic Analysis, Inc., pp. 215-222.
  2. Scerbo, M. 2005 'Biocybemetic systems: Information processing challenges that lie ahead', Proceedings of the 11th International Conference on HumanComputer Interaction.
  3. Cabeza, R., Nyberg, L. 2000, 'Imaging cognition II: An empirical review of 275 PET and fMRI studies', Journal of Cognitive Neuroscience, vol. 12, pp. 1-47.
  4. Karamouzis, S. 2006, 'Artificial Intelligence Applications and Innovations', IFIP Intemational Federation for Information Processing, Vol. 204, Springer, Boston, pp. 417-424.
  5. Sleeman, Brown, J. 1982, Intelligent tutoring systems, New York: Academic Press.
  6. Nicholson, D., Fidopiastis, C., Davis, L., Schmorrow, D. & Stanney, K. 2007, 'An adaptive instructional architecture for training and education', Proceedings of HCI International (in press).
  7. Skinner, B. 1958, Teaching Machines, Science 128, pp. 969-77.
  8. Wenger, E. 1987, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge, Morgan Kaufmann Publishers, Inc., Los Altos, CA.
  9. Anderson, J., Corbett, A., Koedinger, Pelletier, K. 1995, 'Cognitive Tutors: Lessons Learned', Journal of the Learning Sciences, vol. 4, no. 2, pp. 167-207.
  10. Parasuraman, R., Bahri, T., Deaton, J. E., Morrison, J. G., & Barnes, M. 1992, Theory and design of adaptive automation in adaptive systems (Progress Report No. NAWCADWAR-92033-60). Warminster, PA: Naval Air Warfare Center, Aircraft Division.
  11. Paas, F., Renkl, A., & Sweller, J. 2004, 'Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture', Instructional Science, vol. 32, pp. 1-8.
  12. Sweller, J. 1999, Instructional Design in Technical Areas. Australian Council for Educational Research Press, Camberwell, Australia.
  13. Paas, F., Tuovinen, J., Tabbers, H., & Van Gerven, P.W. M. 2003 'Cognitive load measurement as a means to advance cognitive load theory', Educational Psychologist, vol. 38, pp. 63-71.
  14. Hoover, A., Muth, E. 2005, 'A real-time index of vagal activity', International Journal of Human-Computer Interaction, vol. 17 no. 2, pp. 197-209.
  15. Erdogmus, D., Adami, A., Pavel, M., Lan, T., Mathan, S., Whitlow, S., Dorneich, M. 2005, 'Cognitive state estimation based in EEG for augmented cognition', Proceedings of the 2nd International IEEE EMBS Conference in Neural E engineering, Arlington, Virginia, March 16-19.
  16. Downs, J., Downs, T., Robinson, W., Nishimura, E., Stautzenberger, J. 2005, 'A new approach to fNIR: The optical tomographic imaging spectrometer', Proceedings of the 1st International Conference on Augmented Cognition, Las Vegas, NV, 22-27 July 2005.
  17. Berka, C., Levendowski, D., Cvetinovic, M., Davis, G., Lumicao, M., Zickovic, V., Popovic, M., Olmstead, R. 2005, 'Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset', International Journal of Human-Computer Interaction, vol. 17 no. 2, pp. 151-170.
  18. Takahashi, M., Kubo, O., Kitamura, M., Yoshikawa H. 1994, 'Neural network for human cognitive state estimation', Proceedings of the IEEE/RSJ/Gi International Conference on Intelligent Robots and Systems 7894.
  19. Cerutti, S., Bianchi, A., Reiter, H. 2006, 'Analysis of sleep and stress profiles from biomedical signal processing in wearable devices', Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3.
  20. Crosby, M., Ikehara C. 2005, 'Using physiological measures to identify individual differences in response to task attributes', In D.D. Schmorrow, L.M. Reeves, and K.M. Stanney (eds.): Foundations of Augmented Cognition 2nd Edition, Arlington, VA: Strategic Analysis, Inc., pp. 162-168.
  21. Keenan, D., Gorssman, P. 2005, 'Adaptive filtering of heart rate signals for an improved measure of cardiac autonomic control', International Journal of Signal Processing, vol. 2, no. 1, pp. 52-8.
  22. Aysin, B., Aysin, E. 2006, 'Effect of respiration in heart rate variability (HRV) analysis', Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3.
  23. Sleight, P., Casadei, B. 1995, 'Relationships between Heart rate, respiration and blood pressure variabilities', Heart Rate Variability, Futura Publishing Company, Armonk, NY.
  24. Shaltis, P., Reisner, A., Asada, H. 2005, 'Calibration of the photoplethysmogram to arterial blood pressure: capabilities and limitations for continuous pressure monitoring', Proceedings of the 27th IEEE EMBS Annual International Conference, Shanghai, China, Sept 1-4.
  25. Salvucci, D., Anderson, J. 1998, 'Tracing eye movement protocols with cognitive process models', Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, pp. 923-8.
  26. Marshall, S., 2007, 'Identifying cognitive state from eye metrics', Aviation, Space and Environmental Medicine, Vol. 78, no. 5, pp. 165-75.
  27. Lang, P., Bradley, M., Cuthbert B. 2005, 'International affective picture system (IAPS): Affective ratings of pictures and instructional manual”, technical report A6, University of Florida, Gainesville, FL.
Download


Paper Citation


in Harvard Style

A. Vartak A., M. Fidopiastis C., M. Nicholson D., B. Mikhael W. and D. Schmorrow D. (2008). COGNITIVE STATE ESTIMATION FOR ADAPTIVE LEARNING SYSTEMS USING WEARABLE PHYSIOLOGICAL SENSORS . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 147-152. DOI: 10.5220/0001068601470152


in Bibtex Style

@conference{biosignals08,
author={Aniket A. Vartak and Cali M. Fidopiastis and Denise M. Nicholson and Wasfy B. Mikhael and Dylan D. Schmorrow},
title={COGNITIVE STATE ESTIMATION FOR ADAPTIVE LEARNING SYSTEMS USING WEARABLE PHYSIOLOGICAL SENSORS},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={147-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001068601470152},
isbn={978-989-8111-18-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)
TI - COGNITIVE STATE ESTIMATION FOR ADAPTIVE LEARNING SYSTEMS USING WEARABLE PHYSIOLOGICAL SENSORS
SN - 978-989-8111-18-0
AU - A. Vartak A.
AU - M. Fidopiastis C.
AU - M. Nicholson D.
AU - B. Mikhael W.
AU - D. Schmorrow D.
PY - 2008
SP - 147
EP - 152
DO - 10.5220/0001068601470152