Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals
Understanding the driver state (e.g., fatigue state) is critical in improving the safety of urban rail transit operations. This study reports a study that used the simulation experiment to evaluate the effects of fatigue on professional drivers’ psychological, biological, and physical res...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10852214/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859632077012992 |
|---|---|
| author | Hao Wu Yubo Jiao Chaozhe Jiang Tong Wang Jiangbo Yu |
| author_facet | Hao Wu Yubo Jiao Chaozhe Jiang Tong Wang Jiangbo Yu |
| author_sort | Hao Wu |
| collection | DOAJ |
| description | Understanding the driver state (e.g., fatigue state) is critical in improving the safety of urban rail transit operations. This study reports a study that used the simulation experiment to evaluate the effects of fatigue on professional drivers’ psychological, biological, and physical responses and thus develop a fatigue detection model, enabling early warning and ensuring operational safety. Specifically, we applied statistical analytics on self-reported fatigue feelings, the Mackworth Clock Task, the Stroop Test, and the Iowa Gambling Task to evaluate the impact of fatigue on driver performance. The results show that fatigue reduced the participants’ reaction time and cognitive ability and led professional drivers to make more conservative decisions. Moreover, we utilized heart rate signals, electrodermal activity, and eye movements from wearable devices and cameras to build a driver fatigue detection model with machine-learning methods. We analyzed the impact of different time window lengths on fatigue detection. The results indicate that as the length of the time window increases, the data captures more comprehensive information, leading to improved fatigue detection accuracy. Furthermore, multi-feature fusion significantly enhanced model performance. With feature fusion, even short-term time windows can encapsulate sufficient information for effective fatigue detection. These findings not only contribute to a deeper understanding of the risks posed by fatigue in professional driving, but also pave the way to developing a real-time, non-invasive fatigue detection system, enhancing traffic safety. |
| format | Article |
| id | doaj-art-ab55b438ffe1402f9d42da5b01eec052 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ab55b438ffe1402f9d42da5b01eec0522025-02-11T00:00:54ZengIEEEIEEE Access2169-35362025-01-0113232702328410.1109/ACCESS.2025.353348710852214Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response SignalsHao Wu0https://orcid.org/0000-0001-8357-2925Yubo Jiao1https://orcid.org/0000-0001-7133-4567Chaozhe Jiang2Tong Wang3Jiangbo Yu4The Smart City Research Institute of China Electronics Technology Group Corporation, Shenzhen, ChinaDepartment of Civil Engineering, McGill University, Montreal, QC, CanadaSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, ChinaDepartment of Civil Engineering, McGill University, Montreal, QC, CanadaUnderstanding the driver state (e.g., fatigue state) is critical in improving the safety of urban rail transit operations. This study reports a study that used the simulation experiment to evaluate the effects of fatigue on professional drivers’ psychological, biological, and physical responses and thus develop a fatigue detection model, enabling early warning and ensuring operational safety. Specifically, we applied statistical analytics on self-reported fatigue feelings, the Mackworth Clock Task, the Stroop Test, and the Iowa Gambling Task to evaluate the impact of fatigue on driver performance. The results show that fatigue reduced the participants’ reaction time and cognitive ability and led professional drivers to make more conservative decisions. Moreover, we utilized heart rate signals, electrodermal activity, and eye movements from wearable devices and cameras to build a driver fatigue detection model with machine-learning methods. We analyzed the impact of different time window lengths on fatigue detection. The results indicate that as the length of the time window increases, the data captures more comprehensive information, leading to improved fatigue detection accuracy. Furthermore, multi-feature fusion significantly enhanced model performance. With feature fusion, even short-term time windows can encapsulate sufficient information for effective fatigue detection. These findings not only contribute to a deeper understanding of the risks posed by fatigue in professional driving, but also pave the way to developing a real-time, non-invasive fatigue detection system, enhancing traffic safety.https://ieeexplore.ieee.org/document/10852214/Driver fatiguefatigue detectionfeature fusionmachine learningtraffic safety |
| spellingShingle | Hao Wu Yubo Jiao Chaozhe Jiang Tong Wang Jiangbo Yu Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals IEEE Access Driver fatigue fatigue detection feature fusion machine learning traffic safety |
| title | Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals |
| title_full | Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals |
| title_fullStr | Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals |
| title_full_unstemmed | Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals |
| title_short | Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals |
| title_sort | fatigue state evaluation of urban railway transit drivers using psychological biological and physical response signals |
| topic | Driver fatigue fatigue detection feature fusion machine learning traffic safety |
| url | https://ieeexplore.ieee.org/document/10852214/ |
| work_keys_str_mv | AT haowu fatiguestateevaluationofurbanrailwaytransitdriversusingpsychologicalbiologicalandphysicalresponsesignals AT yubojiao fatiguestateevaluationofurbanrailwaytransitdriversusingpsychologicalbiologicalandphysicalresponsesignals AT chaozhejiang fatiguestateevaluationofurbanrailwaytransitdriversusingpsychologicalbiologicalandphysicalresponsesignals AT tongwang fatiguestateevaluationofurbanrailwaytransitdriversusingpsychologicalbiologicalandphysicalresponsesignals AT jiangboyu fatiguestateevaluationofurbanrailwaytransitdriversusingpsychologicalbiologicalandphysicalresponsesignals |