Evaluation of user/operator stress using eye-tracking with machine learning algorithms

Machine Learning (ML) is an ever-growing field that seen a bloom in recent years since the emergence of ChatGPT. Multiple studies had been done on the impact of ML on interpreting human behaviours. A lot of human factors could affect a person’s work performance, including stress, cognitive workload,...

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Main Author: Song, Ke Yan
Other Authors: Chen Chun-Hsien
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/177677
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1776772024-06-01T16:52:17Z Evaluation of user/operator stress using eye-tracking with machine learning algorithms Song, Ke Yan Chen Chun-Hsien School of Mechanical and Aerospace Engineering MCHchen@ntu.edu.sg Engineering Stress Eye tracking Machine learning Machine Learning (ML) is an ever-growing field that seen a bloom in recent years since the emergence of ChatGPT. Multiple studies had been done on the impact of ML on interpreting human behaviours. A lot of human factors could affect a person’s work performance, including stress, cognitive workload, fatigue, attention span, and anxiety. In this study, a stress evaluation model will be developed from eye tracking data to evaluate the stress levels of Vessel Traffic Operators. Eye trackers record multiple metrics from eye movements such as: Pupil Diameter (PD), eye openness, eye positions, fixation points and movement time. By using data collected while inducing different stress levels to participants with Stroop task, a proposed set of features will be extracted and tested on 6 chosen ML algorithms. Our study has shown that SVM performs well with features set with PD, fixation, and saccade. The proposed algorithms achieved classification accuracy of 86.24% with subject-independent training, which outperformed the best methods from other studies. Hypothesis testing was done to further prove the significance of the proposed algorithms in achieving higher classification accuracy with 95% confidence interval. Bachelor's degree 2024-05-30T13:06:07Z 2024-05-30T13:06:07Z 2024 Final Year Project (FYP) Song, K. Y. (2024). Evaluation of user/operator stress using eye-tracking with machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177677 https://hdl.handle.net/10356/177677 en A007 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Stress
Eye tracking
Machine learning
spellingShingle Engineering
Stress
Eye tracking
Machine learning
Song, Ke Yan
Evaluation of user/operator stress using eye-tracking with machine learning algorithms
description Machine Learning (ML) is an ever-growing field that seen a bloom in recent years since the emergence of ChatGPT. Multiple studies had been done on the impact of ML on interpreting human behaviours. A lot of human factors could affect a person’s work performance, including stress, cognitive workload, fatigue, attention span, and anxiety. In this study, a stress evaluation model will be developed from eye tracking data to evaluate the stress levels of Vessel Traffic Operators. Eye trackers record multiple metrics from eye movements such as: Pupil Diameter (PD), eye openness, eye positions, fixation points and movement time. By using data collected while inducing different stress levels to participants with Stroop task, a proposed set of features will be extracted and tested on 6 chosen ML algorithms. Our study has shown that SVM performs well with features set with PD, fixation, and saccade. The proposed algorithms achieved classification accuracy of 86.24% with subject-independent training, which outperformed the best methods from other studies. Hypothesis testing was done to further prove the significance of the proposed algorithms in achieving higher classification accuracy with 95% confidence interval.
author2 Chen Chun-Hsien
author_facet Chen Chun-Hsien
Song, Ke Yan
format Final Year Project
author Song, Ke Yan
author_sort Song, Ke Yan
title Evaluation of user/operator stress using eye-tracking with machine learning algorithms
title_short Evaluation of user/operator stress using eye-tracking with machine learning algorithms
title_full Evaluation of user/operator stress using eye-tracking with machine learning algorithms
title_fullStr Evaluation of user/operator stress using eye-tracking with machine learning algorithms
title_full_unstemmed Evaluation of user/operator stress using eye-tracking with machine learning algorithms
title_sort evaluation of user/operator stress using eye-tracking with machine learning algorithms
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177677
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