Contribution of Biosensors to Enhancing Performance for Users with Special Needs

  • Thanh Truc T. Nguyen
  • Martha E. Crosby
  • Marie Iding
  • Neil G. Scott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


This paper describes two lines of research addressing the use of biosensors for populations with disabilities. The first line of research focused on deriving changing cognitive state information from the patterns of data acquired from users with the goal of improving presentation of multimedia computer information. Detecting individual differences via performance and psychometric tools can be supplemented by using real-time physiological sensors such as eye tracking and pressure applied to a computer mouse. We describe a computer task that demonstrates how to identify cognitive state and discuss types of physiological and cognitive state measures and associated advantages and disadvantages. Adaptive information filtering is discussed as a model for using the physiological information to improve individual performance. In the second line of research we interviewed participants with disabilities in an engineering vocational training program about their needs and suggestions for assistive devices that incorporate biosensors.


biosensors special needs cognition physiological sensors adaptive information filtering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Charlton, S.: Measurement of Cognitive States in Testing and Evaluation. In: Charlton, S.G., O’Brien, T.C. (eds.) Handbook of Human Factors and Evaluation, pp. 97–126 (2002)Google Scholar
  2. 2.
    Card, S., Moran, T., Newell, A.: The Model Human Processor. In: Boff, K., Kaufman, L., Thomas, J. (eds.) Handbook of Perception and Human Performance, vol. 2, pp. 45–41. Wiley, New York (1986)Google Scholar
  3. 3.
    Dumais, S.T., Wright, A.L.: Reference by Name vs. Location in a Computer Filing System. In: Proceedings of the Human Factors Society 30th Annual Meeting, pp. 824–828 (1986)Google Scholar
  4. 4.
    Friedhoff, R.M., Benzon, W.: The second computer revolution: Visualization. Abrams, New York (1989)Google Scholar
  5. 5.
    Egan, D.: Individual Differences in Human-Computer Interaction. In: Helender, M. (ed.) Handbook of Human-Computer Interaction, pp. 543–580. Elsevier, New York (1988)Google Scholar
  6. 6.
    Egan, D.E., Gomez, L.M.: Assaying, Isolating, and Accommodating Individual Differences in Learning a Complex Skill. Individual Differences in Cognition 2, 174–217 (1985)Google Scholar
  7. 7.
    Ekstrom, R.B., French, J.W., Harman, H.H.: Manual for kit of factor-referenced cognitive tests. Educational Testing Service, Princetown (1976)Google Scholar
  8. 8.
    Vincente, B., Hayes, W.R.: Assaying and Isolating Individual Differences in Searching a Hierarchical File System. Human Factors 29, 647–668 (1987)Google Scholar
  9. 9.
    Gomez, L., Egan, D., Wheeler, E., Sharma, D., Gruchaz, A.: How Interface Design Determines Who Has Difficulty Learning to Use a Text Editor. In: Proceedings of Computer-Human Interaction (CHI), pp. 219–222 (1983)Google Scholar
  10. 10.
    Mayer, R., Sims, V.: For Whom Is a Picture Worth a Thousand Words? Extensions of a Dual-coding Theory of Multimedia Learning. Journal of Educational Psychology 86(3), 389–401 (1994)CrossRefGoogle Scholar
  11. 11.
    Sein, M., Bostrom, R.: Individual Differences and Conceptual Models in Training Novice Users. Human-Computer Interaction 4, 197–229 (1989)CrossRefGoogle Scholar
  12. 12.
    Crosby, M., Iding, M.: A Comparison of Two Individual Differences Measures and Performance on a Multimedia Tutor for Learning Physics. Computers and Education 29(23), 127–136 (1997)CrossRefGoogle Scholar
  13. 13.
    Crosby, M., Iding, M.: The influence of cognitive styles on the effectiveness of a multimedia tutor. Computer Assisted Language Learning, Swets & Zeitlinger, Lisse 10(4), 375–386 (1997)CrossRefGoogle Scholar
  14. 14.
    Crosby, M.E., Iding, M.K., Chin, D.N.: Visual search and background complexity: Does the forest hide the trees? In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, pp. 225–227. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Crosby, M., Stelovsky, J.: From multimedia instruction to multimedia evaluation. Journal of Educational Multimedia and Hypermedia 4(2/3), 147–162 (1995)Google Scholar
  16. 16.
    Crosby, M., Stelovsky, J., Ashworth, D.: Hypermedia as a Facilitator for Retention: a Case Study using Kanji City. Computer Assisted Language Learning, Swets & Zeitlinger, Lisse 7(1), 3–13 (1994)CrossRefGoogle Scholar
  17. 17.
    Crosby, M., Stelovsky, J., Ashworth, D.: Predicting Language Proficiency Based on the Use of Multimedia Interfaces for Transcription Tasks. Computer Assisted Language Learning, Swets & Zeitlinger, Lisse 9(2-3), 251–262 (1996)CrossRefGoogle Scholar
  18. 18.
    Crosby, M., Stelovsky, J.: Subject Differences in the Reading of Computer Algorithms. In: Salvendy, G., Smit, M. (eds.) Designing and Using Human-Computer Interfaces and Knowledge Based Systems, pp. 137–144. Elsevier Science, Amsterdam (1989)Google Scholar
  19. 19.
    Crosby, M., Stelovsky, J.: How Do We Read Algorithms? A case study. IEEE Computer 23, 24–35 (1990)CrossRefGoogle Scholar
  20. 20.
    Crosby, M., Chin, D.: Evaluating Multi-user Interfaces (EMI). In: Smith, M., Salvendy, G., Koubek, R. (eds.) Design of Computing Systems: Social and Ergonomic Considerations, vol. 21B, pp. 675–678. Elsevier Science, Amsterdam (1997)Google Scholar
  21. 21.
    Crosby, M., Chin, D.: Investigating User Comprehension of Complex Multi-user Interfaces. In: Bullinger, H.J., Ziegler, J. (eds.) Human-Computer Interaction: Ergonomics and User Interfaces, pp. 856–860. Lawrence Erlbaum, London (1999)Google Scholar
  22. 22.
    Nordbotten, J., Crosby, M.: Individual user Differences in Data Model Comprehension. In: Smith, M., Salvendy, G., Koubek, R. (eds.) Design of Computing Systems: Social and Ergonomic Considerations, vol. 21B, pp. 663–670. Elsevier Science, Amsterdam (1997)Google Scholar
  23. 23.
    Nordbotten, J., Crosby, M.: The Effect of Graphic Style on Data Model Interpretation. Information Systems Journal 9, 139–155 (1999)CrossRefGoogle Scholar
  24. 24.
    Howard, D.L., Crosby, M.: Snapshots from the Eye: Towards Strategies for Viewing Bibliographic Citations, in Advances in Human Factors/Ergonomics. In: Salvendy, G., Smith, M. (eds.) Human-Computer Interaction: Software and Hardware Interfaces, vol. 19B, pp. 488–493. Elsevier Science, Amsterdam (1993)Google Scholar
  25. 25.
    Crosby, M., Peterson, W.: Using eye movements to classify search strategies. Proceedings of the Human Factors Society 2, 1476–1480 (1991)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thanh Truc T. Nguyen
    • 1
  • Martha E. Crosby
    • 2
  • Marie Iding
    • 3
  • Neil G. Scott
    • 1
  1. 1.College of Education, Curriculum Research & Development GroupUniversity of Hawaii at ManoaHonoluluHawaii
  2. 2.College of Natural Sciences, Department of Information and Computer SciencesUniversity of Hawaii at ManoaHonoluluHawaii
  3. 3.College of Education, Department of Educational PsychologyUniversity of Hawaii at ManoaHonoluluHawaii

Personalised recommendations