An Efficient Classification Algorithm for Employee Well-Being Prediction Using Deep Learning

ABSTRACT

The impact of COVID-19 has changed the way work is being done especially in the IT sector. The emergence of work from home as an option has resulted in the evolution of hybrid work culture going forward as the world is moving towards endemic. On these circumstances there has been drastic change in work pattern of employees which clearly impacted the efficiency levels and their wellbeing (both physical and mental). It has also become imperative for the employers to track the efficiency of employees during their working hours in order to ensure maximum productivity in hybrid working model. This paper proposes a system that can detect and track the employee efficiency though facial landmarks by assessing the parameters like drowsiness and stress using deep learning techniques and hybridization of classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.