Evaluation of the user/operator stress using heart rate with machine learning algorithms

• Main Author: Tan, Joanne Si Jie
• Other Authors: Chen Chun-Hsien
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Industrial engineering::Human factors engineering; Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
• Online Access: https://hdl.handle.net/10356/167900
• Institution: Nanyang Technological University

It has been demonstrated that psychological stress can trigger physiological responses, including changes in heart rate. The ability to recognise stress levels through the measurement of heart rate may prove useful for workers in demanding occupations that operate in highly stressful environments, such as air traffic controllers or in the healthcare industry. The advancements in Machine Learning and Data Science have enabled the development of techniques that can assist in identifying and predicting levels of mental workload, stress, fatigue, and emotions in humans. In addition to electroencephalograms (EEGs), electrocardiograms (ECGs), and eye tracking, bio signals can also be obtained. AI systems based on bio signals can be used to gain a deeper understanding of the working routine of a subject. This project aims to propose a real-time algorithm to recognise stress from heart rate in marine ports utilising machine learning techniques.