Sleepiness detection in daily life based on multimodal signals

Binwen Li; Miao Liu; Banghao Cai; Jiayu Li; Wanhui Wen

doi:10.1117/12.3037603

27 September 2024 Sleepiness detection in daily life based on multimodal signals

Binwen Li, Miao Liu, Banghao Cai, Jiayu Li, Wanhui Wen

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132750W (2024) https://doi.org/10.1117/12.3037603
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

This study employs machine learning methods with the aim of detecting daily-life sleepiness in the daily study life of college students, facilitating individuals to adjust their states sensibly and enhance work efficiency. Initially, we collected electrocardiogram (ECG) and tri-axial accelerometer data from 83 college students during their daily activities. We extracted 21 features from the ECG and 3 features representing the level of physical activity from the tri-axial accelerometer data. Subsequently, we trained five classifiers using these features and employed a backward selection algorithm through Leave-One-Subject-Out (LOSO) cross-validation to choose the most crucial feature subset. Ultimately, utilizing the best-performing classifier and the selected key feature subset, we constructed a model for detecting daily-life sleepiness, achieving the F1 score of 77.61% on the independent test set. The results suggest that employing machine learning to detect the sleepiness of individuals is feasible.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Binwen Li, Miao Liu, Banghao Cai, Jiayu Li, and Wanhui Wen "Sleepiness detection in daily life based on multimodal signals", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132750W (27 September 2024); https://doi.org/10.1117/12.3037603

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