An Innovative Deep Neural Network for Stress Classification in Workplace

Abstract

Human Resource & Management (HRM) plays a vital role in organizational operations. The HRM tries to produce optimal output from human resources through workload balance. One of the core factors of workload balance is stress management. Although Deep Learning technology has introduced revolutionary applications in different sectors, its application in HRM is still nominal. This paper proposes an innovative application of Deep Learning to classify stressed and satisfied employees automatically. This generalized adaptive method utilizes quantitative measures which ensure unbiased classification with 88.40% accuracy and 0.8728 F1-score. The proposed network outperforms similar approaches, paving the path to applying Deep Learning based solutions to ensure a better workplace and proper workload balance through an effortless automatic but reliable stress classifier.