Editorial
Affective computing in ambient intelligence systems

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Research on affective computing and its applications

Research on emotion is a fascinating yet complex field. Historically, it has included philosophy, psychology, sociology, and more recently cognitive science, neuroscience, and finally artificial intelligence and computer science. Today, Affective Computing (AfC) is an established and mature paradigm in computer science, that has been an area of very active interdisciplinary research. It is often considered, that the beginning and foundation of the AfC field, was the publishing of the paramount

The AfCAI research community

The AfCAI research community1 is an informal open group of researcher from many countries, that was established by the organization of the AfCAI workshops as well as other activities. The abbreviation stands for Affective Computing and Context Awareness in Ambient Intelligence. The principal objectives of the AfCAI workshop series is to put selected problems and methods from the area of AfC into a specific engineering context. Our focus is to consider use

Contents of the special issue

The motivation for the open call for this special issue was to present current research in the selected areas of the AfCAI workshop. Furthermore, we wanted to extend the AfCAI community by attracting other researchers. The goal for this special issue is to bring together researchers from several domains related to the development of affective computing methods and their applications. The objective was to select and publish state-of-art research findings in the recent developments regarding

Summary

Affective technologies can contribute in an essential way to the development of future generation of computer systems that will be able to detect, recognize and interpret human emotions. Furthermore, they will be able to synthesize emotional responses to advance human–computer interaction. This direction is very relevant to the recent discussion in AI regarding the so-called Humanized Artificial Intelligence (HAI). Computer systems using HAI will play in important role in the next generation of

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    • Cited by (3)

    • Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals

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      However, information gathered from physiological signals have proven to be of great importance in sentiment analysis as it gives unbiased result through autonomous body reaction (Novak et al., 2012). The recent advancement on context-aware multimodal emotion detection with mobile and wearable devices can be found in details in the research works by (Costa et al., 2019; Lee and Jung, 2019; Nalepa et al., 2019a; Nalepa et al., 2019b; Przybyło et al., 2019). In an early research by (Collet et al., 1997) pictures which were neutral and emotionally loaded were shown to participants in order to elicit happiness, surprise, anger, fear, sadness, and disgust.

    • An Academic Affect Dataset: Spontaneous Facial Expressions and Head poses Collected during Online Examination

      2020, ACM International Conference Proceeding Series

    • Adjectives grouping in a dimensionality affective clustering model for fuzzy perceptual evaluation

      2020, International Journal of Interactive Multimedia and Artificial Intelligence
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