Affective Modeling from Multichannel Physiology: Analysis of Day Differences
Physiological signals are widely considered to contain affective information. Consequently, pattern recognition techniques such as classification are commonly used to detect affective states from physiological data. Previous studies have achieved some success in detecting affect from physiological measures, especially in controlled environments where emotions are experimentally induced. One challenge that arises is that physiological measures are expected to exhibit considerable day variations due to a number of extraneous factors such as environmental changes and sensor placements. These variations pose challenges to effectively classify affective sates from future physiological data; this is a common problem for real world requirements. The present study provides a quantitative analysis of day variations of physiological signals from different subjects. We propose a classifier ensemble approach using a Winnow algorithm to address the problem of day-variation in physiological signals. Our results show that the Winnow ensemble approach outperformed a static classification approach for detecting affective states from physiological signals that exhibited day variations.
KeywordsAffect detection classifier ensembles non-stationarity physiology
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