Abstract
Different LSTM-based models were tested for binary drowsiness detection using the ULg Multimodality Drowsiness Database (DROZY). The dataset contains physiological signals and behavioral measures collected from participants during different experimental conditions designed to induce varying levels of drowsiness. The LSTM models were trained using a sequential approach using the inter-beat intervals, where they were exposed to increasing levels of drowsiness over time. The performance of the models was evaluated in terms of accuracy, precision, recall, F1-score, and AUC. The results showed that the stacked bidirectional LSTM model achieved the highest performance with an accuracy of 0.873, precision of 0.825, recall of 0.793, F1-score of 0.808, and AUC of 0.918. These findings suggest that LSTM-based models can learn to capture the temporal dynamics of drowsiness and make accurate predictions based on the current and previous levels of drowsiness.
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References
Baharav, A., et al.: Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variability. Neurology 45, 1183–1187 (1995)
Carreiras, C., et al.: BioSPPy: Biosignal Processing in Python (2015). https://github.com/PIA-Group/BioSPPy/
Commission, E.: Road safety thematic report - fatigue. Technical report, European Commission, Directorate General for Transport (2021)
European Parliament, Council of the European Union: Regulation (EU) 2019/2144 of the European Parliament and of the Council, November 2019
Halin, A., Verly, J.G., Droogenbroeck, M.V.: Survey and synthesis of state of the art in driver monitoring. Sensors 21 (2021). https://doi.org/10.3390/s21165558
Hamilton, P.: Open source ECG analysis. In: Computers in Cardiology, pp. 101–104, September 2002. https://doi.org/10.1109/CIC.2002.1166717, iSSN 0276-6547
Iwamoto, H., Hori, K., Fujiwara, K., Kano, M.: Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder. IFAC-PapersOnLine 54, 526–531 (2021). https://doi.org/10.1016/j.ifacol.2021.10.310
Kaida, K., et al.: Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin. Neurophysiol. Off. J. Int. Federation Clin. Neurophysiol. 117, 1574–1581 (2006). https://doi.org/10.1016/j.clinph.2006.03.011
Lourenço, A., Alves, A.P., Carreiras, C., Duarte, R.P., Fred, A.: Cardiowheel: ECG biometrics on the steering wheel. Mach. Learn. Knowl. Discov. Databases, 267–270 (2015)
Massoz, Q., Langohr, T., Francois, C., Verly, J.G.: The ULg multimodality drowsiness database (called drozy) and examples of use. In: Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016), Lake Placid, NY (2016)
Oliveira, L., Cardoso, J.S., Lourenço, A., Ahlström, C.: Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods. In: 2018 7th European Workshop on Visual Information Processing (EUVIP), pp. 1–6 (2018). https://doi.org/10.1109/EUVIP.2018.8611704
Oliveira, L., Cardoso, J.S., Lourenço, A., Ahlström, C.: Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods. In: Proceedings - European Workshop on Visual Information Processing, EUVIP 2018-November (2019). https://doi.org/10.1109/EUVIP.2018.8611704
Philip, P., et al.: Effect of fatigue on performance measured by a driving simulator in automobile drivers. J. Psychosomatic Res. 55, 197–200 (2003). https://doi.org/10.1016/S0022-3999(02)00496-8
Rodrigues, L.: Driver Drowsiness Detection with Peripheral Cardiac Signals. Master’s thesis, Instituto Superior Técnico, Lisbon, Portugal (2021)
Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Publ. Health 5, 258 (2017). https://doi.org/10.3389/fpubh.2017.00258, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624990/
Silveira, C.S., Cardoso, J.S., Lourenço, A.L., Ahlström, C.: Importance of subject-dependent classification and imbalanced distributions in driver sleepiness detection in realistic conditions. IET Intell. Transp. Syst. 13 (2019). https://doi.org/10.1049/iet-its.2018.5284
Sztajzel, J.: Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. Swiss Med. Wkly 134, 514–522 (2004)
Talbot, R., et al.: Framework for operational design of experimental work in i-dreams. Deliverable 3.1 of the EC H2020 project iDREAMS (2020)
Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into deep learning. CoRR abs/2106.11342 (2021). https://arxiv.org/abs/2106.11342
Čulík, K., Kalašová, A., Štefancová, V.: Evaluation of driver’s reaction time measured in driving simulator. Sensors 22 (2022). https://doi.org/10.3390/s22093542
Funding
This work was partially funded by the Instituto de Telecomunicações (IT), by the Fundação para a Ciência e Tecnologia (FCT) under the project UIDB/50008/2020 and under the Scientific Employment Stimulus Individual Call grant 2022.04901.CEECIND, and by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Programme (COMPETE 2020), and by National Funds (OE) through the FCT under the LISBOA-01-0247-FEDER-069918 “CardioLeather”.
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Silva, R., Abrunhosa Rodrigues, L., Lourenço, A., Plácido da Silva, H. (2023). Temporal Dynamics of Drowsiness Detection Using LSTM-Based Models. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_17
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