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Article

Reducing Videoconferencing Fatigue through Facial Emotion Recognition

1
Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany
2
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
3
MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mehrdad Jalali
Future Internet 2021, 13(5), 126; https://doi.org/10.3390/fi13050126
Received: 17 April 2021 / Revised: 10 May 2021 / Accepted: 10 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue Social Networks Analysis and Mining)
In the last 14 months, COVID-19 made face-to-face meetings impossible and this has led to rapid growth in videoconferencing. As highly social creatures, humans strive for direct interpersonal interaction, which means that in most of these video meetings the webcam is switched on and people are “looking each other in the eyes”. However, it is far from clear what the psychological consequences of this shift to virtual face-to-face communication are and if there are methods to alleviate “videoconferencing fatigue”. We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings. Our experimental setting consisted of 35 participants collaborating in eight teams over Zoom in a one semester course on Collaborative Innovation Networks in bi-weekly video meetings, where each team presented its progress. Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise). Our dependent variable was a score given after each presentation by all participants except the presenter. We found that the happier the speaker is, the happier and less neutral the audience is. More importantly, we found that the presentations that triggered wide swings in “fear” and “joy” among the participants are correlated with a higher rating. Our findings provide valuable input for online video presenters on how to conduct better and less tiring meetings; this will lead to a decrease in “videoconferencing fatigue”. View Full-Text
Keywords: facial emotion recognition; social network analysis; video meetings facial emotion recognition; social network analysis; video meetings
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MDPI and ACS Style

Rößler, J.; Sun, J.; Gloor, P. Reducing Videoconferencing Fatigue through Facial Emotion Recognition. Future Internet 2021, 13, 126. https://doi.org/10.3390/fi13050126

AMA Style

Rößler J, Sun J, Gloor P. Reducing Videoconferencing Fatigue through Facial Emotion Recognition. Future Internet. 2021; 13(5):126. https://doi.org/10.3390/fi13050126

Chicago/Turabian Style

Rößler, Jannik, Jiachen Sun, and Peter Gloor. 2021. "Reducing Videoconferencing Fatigue through Facial Emotion Recognition" Future Internet 13, no. 5: 126. https://doi.org/10.3390/fi13050126

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