Fatigue-related situation awareness recognition using advanced machine learning

Situation awareness (SA) is a complex state of mind that results in physical and psychological changes on operators in various dynamic systems. Therefore, a precise measurement method of SA is the first priority for keeping the safety of the systems. Compared with traditional SA assessment technique...

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Main Author: Li, Ruilin
Other Authors: Wang Lipo
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/175644
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1756442024-06-03T06:51:19Z Fatigue-related situation awareness recognition using advanced machine learning Li, Ruilin Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Computer and Information Science Engineering Situation awareness (SA) is a complex state of mind that results in physical and psychological changes on operators in various dynamic systems. Therefore, a precise measurement method of SA is the first priority for keeping the safety of the systems. Compared with traditional SA assessment techniques, Electroencephalography (EEG) has the key advantages of high time resolution, non-intrusiveness and being objective, which are more suitable for practical applications. This research aims to improve the end-to-end cross-subject SA recognition performance from the following aspects: dealing with the problems of subject variability, improving the efficiency of the backbone models, high complexity of input signals and data scarcity. Furthermore, multi-modal (EEG + eye tracking) SA recognition is also performed. Compared to the traditional calibration-based EEG processing, the methods for calibration-free and automated decoding from raw EEG signals are provided. This research is meaningful to the future practical use of EEG signals for SA recognition in various dynamic systems. Doctor of Philosophy 2024-05-02T04:50:23Z 2024-05-02T04:50:23Z 2024 Thesis-Doctor of Philosophy Li, R. (2024). Fatigue-related situation awareness recognition using advanced machine learning. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175644 https://hdl.handle.net/10356/175644 10.32657/10356/175644 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). 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 Computer and Information Science
Engineering
spellingShingle Computer and Information Science
Engineering
Li, Ruilin
Fatigue-related situation awareness recognition using advanced machine learning
description Situation awareness (SA) is a complex state of mind that results in physical and psychological changes on operators in various dynamic systems. Therefore, a precise measurement method of SA is the first priority for keeping the safety of the systems. Compared with traditional SA assessment techniques, Electroencephalography (EEG) has the key advantages of high time resolution, non-intrusiveness and being objective, which are more suitable for practical applications. This research aims to improve the end-to-end cross-subject SA recognition performance from the following aspects: dealing with the problems of subject variability, improving the efficiency of the backbone models, high complexity of input signals and data scarcity. Furthermore, multi-modal (EEG + eye tracking) SA recognition is also performed. Compared to the traditional calibration-based EEG processing, the methods for calibration-free and automated decoding from raw EEG signals are provided. This research is meaningful to the future practical use of EEG signals for SA recognition in various dynamic systems.
author2 Wang Lipo
author_facet Wang Lipo
Li, Ruilin
format Thesis-Doctor of Philosophy
author Li, Ruilin
author_sort Li, Ruilin
title Fatigue-related situation awareness recognition using advanced machine learning
title_short Fatigue-related situation awareness recognition using advanced machine learning
title_full Fatigue-related situation awareness recognition using advanced machine learning
title_fullStr Fatigue-related situation awareness recognition using advanced machine learning
title_full_unstemmed Fatigue-related situation awareness recognition using advanced machine learning
title_sort fatigue-related situation awareness recognition using advanced machine learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175644
_version_ 1800916099113418752