EEG Based Aptitude Detection System for Stress Regulation in Health Care Workers

Abstract

Stress is a complex multifaceted concept that is the result of adverse or demanding circumstances. Workers, especially health care workers, suffer significantly from distress, burnout, and other physical illnesses such as hypertension and diabetes caused by stress. Numerous stress detection systems are realized but they only help in detecting the stress in early stages, and, for regularizing it, these systems employ other means. These systems lack any inherent feature for regularization of stress. In contributing toward this aim, a novel system “EEG-Based Aptitude Detection System” is proposed. This system will help in considering working aptitude of employees working in work places with an intention to help them in assigning proper job roles based on their working aptitude. Selection of right job role for workers not only helps in uplifting productivity but also helps in regulating stress level of employees caused by improper job role assignments and reduces fatigue. Being able to select right job role for workers will help them in providing productive working environment. This paper presents detail layered architecture, implementation details, and outcomes of the proposed novel system. Integration of this system in work places will help supervisors in utilizing the human resource more suitably and will help in regulating stress related issues with improvement in overall performance of entire office. In this work, different implementation architectures based on KNN, SVM, DT, NB, CNN, and LSTM are tested, where LSTM has provided better results and achieved accuracy up to 94% in correctly classifying an EEG signal. The rest of the details can be seen in Sections 3 and 5.