The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 8; p. 2308
• Main Authors: Hazer-Rau, Dilana, Meudt, Sascha, Daucher, Andreas, Spohrs, Jennifer, Hoffmann, Holger, Schwenker, Friedhelm, Traue, Harald C.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.04.2020; MDPI
• Subjects: Acquisitions & mergers; affective corpus; Behavior; Datasets; Emotions; Emotions - physiology; Experiments; frustration; Human performance; Human-computer interaction; Humans; interest; Machine Learning; multimodal sensors; overload; Pattern Recognition, Visual - physiology; Physiology; Sensors; underload; User-Computer Interface; Writing; cognitive load; human-computer interaction; affective computing; interest; frustration; underload; multimodal sensors; affective corpus; stress research; machine learning; emotion recognition; overload

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emo...