A MULTIPURPOSE ARTIFICIAL INTELLIGENCE BASED ATTENTIVENESS MONITORING SYSTEM

Due to the coronavirus disease pandemic, most of us are working and/or learning from home. In most countries, both school students and college students are attending their classes at home using online meeting platforms. The professionals managing their projects and company meetings through online meetings. Most of the universities in India and abroad assessing the students through online tests and it is easy for people to cheat the online examination system in various ways. In the current scenario, an efficient online assessment is very crucial to ensure the quality and integrity of any education system. With this objective, in this paper, we introduce a multipurpose artificial intelligence model-based attentiveness monitoring system that will be continuously monitoring the attentiveness of the candidates who are involving in the assessment by considering various parameters. The proposed scheme tracks the eye movement, head movements in two axes (left, right, up, and down), and also the mouth movement. A trust score of the candidate can be generated based on the predefined threshold values decided by the authority who is conducting the assessment. It is possible to independently adjust the threshold for the three different parameters separately thus avoiding false results. The experimental study of the proposed scheme is carried out on the video samples which are recorded for this purpose. The candidates who participated in the video recording process mimicked the behavior of various categories of candidates.