Evaluation of the user/operator fatigue using heart rate with machine learning algorithms
Psychological fatigue has been shown to be highly related to stress and plays a significant part in workplace accidents and mistakes. Therefore, fatigue monitoring can be vital and important for demanding roles, such as those in a high-stress environment. Together with the advancement in Machine Lea...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/168255 |
| _version_ | 1824454973489938432 |
|---|---|
| author | Hoe, Chang Shen |
| author2 | Chen Chun-Hsien |
| author_facet | Chen Chun-Hsien Hoe, Chang Shen |
| author_sort | Hoe, Chang Shen |
| collection | NTU |
| description | Psychological fatigue has been shown to be highly related to stress and plays a significant part in workplace accidents and mistakes. Therefore, fatigue monitoring can be vital and important for demanding roles, such as those in a high-stress environment. Together with the advancement in Machine Learning and Data Science, techniques can be applied to help recognise and predict levels of human mental workload, stress, fatigue, emotions etc. from biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and eye tracking etc. Such biosignal-based AI systems can be used to properly understand a subject's working routine.
The object of this project to is propose a real-time algorithm of fatigue recognition from heart rate based on machine learning techniques for marine port operators. |
| first_indexed | 2025-02-19T03:30:49Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/168255 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:30:49Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1682552023-06-10T16:52:51Z Evaluation of the user/operator fatigue using heart rate with machine learning algorithms Hoe, Chang Shen Chen Chun-Hsien School of Mechanical and Aerospace Engineering Fraunhofer Singapore Olga Sourina MCHchen@ntu.edu.sg, eosourina@ntu.edu.sg Engineering::Industrial engineering::Human factors engineering Psychological fatigue has been shown to be highly related to stress and plays a significant part in workplace accidents and mistakes. Therefore, fatigue monitoring can be vital and important for demanding roles, such as those in a high-stress environment. Together with the advancement in Machine Learning and Data Science, techniques can be applied to help recognise and predict levels of human mental workload, stress, fatigue, emotions etc. from biosignals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and eye tracking etc. Such biosignal-based AI systems can be used to properly understand a subject's working routine. The object of this project to is propose a real-time algorithm of fatigue recognition from heart rate based on machine learning techniques for marine port operators. Bachelor of Engineering (Mechanical Engineering) 2023-06-09T07:33:43Z 2023-06-09T07:33:43Z 2023 Final Year Project (FYP) Hoe, C. S. (2023). Evaluation of the user/operator fatigue using heart rate with machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168255 https://hdl.handle.net/10356/168255 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Industrial engineering::Human factors engineering Hoe, Chang Shen Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title | Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title_full | Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title_fullStr | Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title_full_unstemmed | Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title_short | Evaluation of the user/operator fatigue using heart rate with machine learning algorithms |
| title_sort | evaluation of the user operator fatigue using heart rate with machine learning algorithms |
| topic | Engineering::Industrial engineering::Human factors engineering |
| url | https://hdl.handle.net/10356/168255 |
| work_keys_str_mv | AT hoechangshen evaluationoftheuseroperatorfatigueusingheartratewithmachinelearningalgorithms |