Next Article in Journal
User Acceptance of Smart Watch for Medical Purposes: An Empirical Study
Next Article in Special Issue
Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
Previous Article in Journal
Towards Practical Applications in Modeling Blockchain System
Previous Article in Special Issue
RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Reducing Videoconferencing Fatigue through Facial Emotion Recognition

Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
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;
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
Show Figures

Figure 1

MDPI and ACS Style

Rößler, J.; Sun, J.; Gloor, P. Reducing Videoconferencing Fatigue through Facial Emotion Recognition. Future Internet 2021, 13, 126.

AMA Style

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

Chicago/Turabian Style

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop