Abstract
The detection of emotion is becoming an increasingly important field for human-computer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively.
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Haag, A., Goronzy, S., Schaich, P., Williams, J. (2004). Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds) Affective Dialogue Systems. ADS 2004. Lecture Notes in Computer Science(), vol 3068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24842-2_4
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DOI: https://doi.org/10.1007/978-3-540-24842-2_4
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