LAUSR

The profuse popularity of video conferencing has led to a simultaneous rise in the opportunity for the participants to multitask. Productive multitasking, such as taking notes, browsing for relevant information, etc., can help promote the cognitive attentiveness of participants. However, existing approaches of tagging inattentive participants solely based on their visual concentration on the meeting app fail to work in such instances. This paper proposes EmotiConf -- a novel real-time framework to monitor participants' attentiveness and a non-real-time framework for visual multitask detection without explicitly relying on their visual concentration. EmotiConf utilizes an unconventional observation where the emotional states of attentive participants, captured through their facial expressions, correlate and also correspond to the vocal expression of the speaker and the intent of the speech. Accordingly, EmotiConf develops a software wrapper to tag the inattentive participants while also characterizing visual multitasking instances performed by them. A thorough evaluation of EmotiConf confirms its usability with a high score of >80.