An empirical study of machine learning techniques for affect recognition in human–robot interaction

Generic Physiological Features as Predictors of Player Experience

“…Several tasks have been employed to elicit emotional responses in experiment participants including watching videos, reading and solving mathematical problems (see [4][5][6] for extensive reviews) but also playing videogames (e.g. [7,8]). …”

“…In this paper we do not focus on linear psychophysiological relationships but, instead, we apply machine learning to create non-linear models that approximate the function between a set of physiological signal attributes and self-reported affective states. While most studies in machine learning within psychophysiology ( [17,18,7,19] among others) focus on the classification accuracies of different methods and disregard the particular models built, this paper analyses the effect of various physiological features in the prediction of affective states.…”

Towards Affect-sensitive Assistive Intervention Technologies for Children with Autism

“…The affective model produced high recognition accuracies for each target affective state of each participant. The average correct prediction accuracies across all participants were: 85.0% for liking, 79.5% for anxiety, and 84.3% for engagement, which are comparable to the best results achieved for typical individuals (Picard et al, 2001;Rani et al, 2006b). We also compared the performance of affective modeling to a control method that represents random chance.…”

“…These signals were selected because they are likely to demonstrate variability as a function of the targeted affective states, as well as they can be measured non-invasively, and are relatively resistant to movement artifacts (Lacey & Lacey, 1958;Dawson et al, 1990). Further details of the physiological signals examined in this work along with the features derived from each signal can be found in our supplementary publication Rani et al (2006b). The physiological signals were acquired using the Biopac MP150 data acquisition system (www.biopac.com).…”

“…Our earlier work (Rani et al, 2006b) compared the efficacy of several machine learning algorithms (KNN, Bayesian Network Technique, Regression Tree, and SVM) to recognize the affective states from the physiological signals of typical individuals and found that SVM gave the highest classification accuracy. In this work, SVM was employed to determine the underlying affective state of a child with ASD given a set of physiological features.…”