Significance of Pupil Diameter Measurements for the Assessment of Affective State in Computer Users
The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of Affective Computing concepts. The determination of the affective state of a computer user from the measurement of some of his/her physiological signals is a promising avenue towards that goal. In addition to the monitoring of signals typically analyzed for affective assessment, such as the Galvanic Skin Response (GSR) and the Blood Volume Pulse (BVP), other physiological variables, such as the Pupil Diameter (PD) may be able to provide a way to assess the affective state of a computer user, in real-time. This paper studies the significance of pupil diameter measurements towards differentiating two affective states (stressed vs. relaxed) in computer users performing tasks designed to elicit those states in a predictable sequence. Specifically, the paper compares the discriminating power exhibited by the pupil diameter measurement to those of other single-index detectors derived from simultaneously acquired signals, in terms of their Receiver Operating Characteristic (ROC) curves.
Unable to display preview. Download preview PDF.
- Picard, R.W. 1997. Affective computing. MIT Press, Cambridge, Mass.Google Scholar
- Beatty, J., Lucero-wagoner, B. 2000. The Pupillary System. In: Handbook of Psychophysiology. J.T. Cacioppo, L.G. Tassinary, and G.G. Berntson, editors. pp. 142-162. Cambridge University PressGoogle Scholar
- Hess, E.H. 1975. The tell-tale eye : how your eyes reveal hidden thoughts and emotions. Van Nostrand Reinhold, New YorkGoogle Scholar
- Grings, W.W., Dawson, M.E. 1978. Emotions and Bodily Responses A psychophysiological Approach. Academic Press, Inc.Google Scholar
- Stern, R.M., Ray, W.J., Quigley, K.S. 2001. Psychophysiological Recording. Oxford University Press.Google Scholar
- Barreto, A. and Zhai, J., “Physiological Instrumentation for Real-time Monitoring of Affective State of Computer Users,” WSEAS Transactions on Circuits and Systems, vol. 3, pp. 496-501, 2003.Google Scholar
- Usui, S. and Amidror, I., “Digital Low-Pass Differentiation for Biological Signal Processing,” IEEE Trans. BME, 29: 686-693, 1982.Google Scholar
- Zhai J., and Barreto A., “Stress Detection in Computer Users Through Noninvasive Monitoring of Physiological Signals “, Biomedical Science Instrumentation, vol. 42, pp. 495-500, 2006..Google Scholar
- Zhai, J., and Barreto, A., “Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables”, Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3, 2006, pp. 1355 – 1358.Google Scholar
- Zhai, J., and Barreto, A., “Stress Recognition Using Non-invasive Technology”, Proceedings 19th Int. Florida Artificial Intelligence Research Society Conference (FLAIRS 2006), pp. 395 – 400, 2006.Google Scholar