Driver fatigue detection using PPG signal, facial features, head postures with an LSTM model
Adam's optimized LSTM fatigue detection model is the most effective, with a model accuracy of 97.36 %, precision of 97.4 %, recall of 97.4 %, and F1 of 0.97. It shows that the model can provide a more timely and accurate prediction and warning for drivers who are already fatigued.
Authors
- Jianguo Liu (ORCID: https://orcid.org/0000-0001-7748-0960)
- Bo Li (ORCID: https://orcid.org/0000-0002-4839-3224)
- Lu Yu
- Xinyi Yang
- Hengjian Wei
Institutions
- Dalian Jiaotong University (CN)
- University of Science and Technology Beijing (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e39479
- Citations
- 24
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 12.18
Funders
- Liaoning Provincial Department of Human Resources and Social Security
- Department of Education of Liaoning Province