| Title: | OS26-1 Driver State Monitoring Using Pose Estimation: Detecting Fatigue, Stress, and Emotional States for Safer Roads |
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| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 717-722 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS26-1 |
| Author(s): | Hao Feng Chan, Dexter Sing Fong Leong, Shakir Hussain Naushad Mohamed, Wui Chung Alton Chau, Andi Prademon Yunus, Takao Ito, Zheng Cai, Xinjie Deng, Yit Hong Choo |
| Publication Date: | February 13, 2025 |
| Keywords: | Driving, Pose estimation, Fatigue, Deep learning, Computer vision |
| Abstract: | Driving under fatigue, stress, or emotional impairment poses significant risks to road safety. This paper proposes a custom pose estimation framework designed to detect driver states, such as fatigue and stress, by analyzing body posture, head pose, and gesture dynamics. Using a novel deep learning approach trained on diverse driving scenarios, the model identifies physiological and behavioral markers associated with impaired states. Unlike existing methods, this system integrates pose estimation with emotional and movement analysis, enabling robust performance in challenging conditions, including poor lighting and occlusions. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS26/OS26-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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