Affective Computing and the Impact of Gender and Age

PLOS ONE, Dec 2019

Affective computing aims at the detection of users’ mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy.

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Affective Computing and the Impact of Gender and Age

March Affective Computing and the Impact of Gender and Age Stefanie Rukavina 0 1 Sascha Gruss 0 1 Holger Hoffmann 0 1 Jun-Wen Tan 0 1 Steffen Walter 0 1 Harald C. Traue 0 1 0 1 Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology, Ulm University , Ulm, Germany , 2 College of Teacher Education, Lishui University , Lishui , P.R. China 1 Editor: Joseph Najbauer, University of Pécs Medical School , HUNGARY Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy. - OPEN ACCESS Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by SFB/ Transregio 62 – Companion Technology for Cognitive Systems project – funded by the German Research Foundation, www.sfb-trr-62.de. Competing Interests: The authors have declared that no competing interests exist. Introduction Affective computing can be described as “computing that relates to, arises from or deliberately influences emotions” [1]. Therefore, it is essential to correctly identify and recognize these human emotional reactions in order to improve the interactions between digital devices and their users. People tend to manifest and communicate emotional reactions during humancomputer interactions (HCI) that display similarities to emotions reported in human-human interactions (HHI) [ 2 ]. Similarities regarding these emotional reactions have been studied in detail [ 3 ]. There are only small discernible differences for, e.g., “disgust,” which is significantly more often reported during HHI, whereas “getting annoyed” is more frequently reported during HCI. To improve HCI by adaptation to individual users’ needs and situations, a research project “SFB/TRR 62” (http://www.sfb-trr-62.de/) is currently pursuing the idea of a companion technology with personalized user models and automated recognition of mental states like emotions, dispositions and intentions. Such companion technologies should not be understood as technical devices, rather, as cognitive digital abilities to adapt individually to their users’ mental states, and trusted as supporting cognitive companion systems [ 4 ]. Overcoming this challenge of recognizing the emotional and dispositional states of a user in a robust manner and with high recognition accuracy, human-computer interactions would thus achieve a higher degree of quality. It would be possible to use such companion technologies as supportive digital companions, e.g., for people with special demands such as elderly individuals, or as elaborated in Walter et al (2013): “its application potential ranges from novel individual operation assistants for the technical equipment to a new generation of versatile organization assistants and digital services and, finally, to innovative support systems, e.g., for patients in rehabilitation or people with limited cognitive abilities” [ 5 ]. Companion technology goes beyond assistive technology if the recognition of users’ mental states is used to adapt to and support the users’ goals through meaningful feedback. Due to the fact that affective computing is a very broad area of research, only a limited number of elements of the general goals have been considered in the past, e.g., measuring psychophysiological parameters in HCI, as well as the process of feature extraction and classification of emotions. However, the impact of different subject-specific variables such as gender, age, personality and gender role in the proces (...truncated)


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Stefanie Rukavina, Sascha Gruss, Holger Hoffmann, Jun-Wen Tan, Steffen Walter, Harald C. Traue. Affective Computing and the Impact of Gender and Age, PLOS ONE, 2016, Volume 11, Issue 3, DOI: 10.1371/journal.pone.0150584