Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality
By integrating this approach into an IoT framework, researchers effectively addressed previous challenges and established an artificial intelligence of things (AIoT) infrastructure for critical driving conditions. This IoT-based system optimizes data processing, reduces computational complexity, and enhances overall system performance, enabling accurate and timely detection of fatigue in extreme driving environments.
Authors
- Mohammad R. Khosravi (ORCID: https://orcid.org/0000-0002-2029-5067)
- Hossein Ghayoumi Zadeh (ORCID: https://orcid.org/0000-0002-5390-3938)
- Hani Attar (ORCID: https://orcid.org/0000-0001-8028-7918)
- Khosro Rezaee (ORCID: https://orcid.org/0000-0001-6763-6626)
- Mohammad Kanan (ORCID: https://orcid.org/0000-0003-0526-0015)
- Asmar Nazerian
Institutions
- Zarqa University (JO)
- Shiraz University of Medical Sciences (IR)
- Vali Asr University of Rafsanjan (IR)
- Mofid University (IR)
- Islamic Azad University, Shahr-e-Qods Branch (IR)
- Weifang University of Science and Technology (CN)
- University of Business and Technology (SA)
Publication Details
- Journal
- Bioimpacts
- Published
- 2024-11-02
- DOI
- https://doi.org/10.34172/bi.30586
- Citations
- 1
- Primary Topic
- Sleep and Work-Related Fatigue
- Type
- article
- Field-Weighted Citation Impact
- 0.51