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.
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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)