Abstract
Insufficient sleep is a prominent problem in modern society with several negative effects and risks. One of the most serious consequences is traffic accidents caused by drowsy driving. Current solutions are focused on detecting drowsiness, where individuals need to reach a certain drowsiness level to receive an alarm, which may be too late to react. In this context, it is relevant to develop a wearable system that integrates the prediction of drowsiness and its prevention. By predicting the drowsy state, the driver can be warned in advance while still alert. To minimize further incidents, the reason why a state of drowsiness occurs must be identified, caused by a sleep disorder or sleep deprivation. The contribution of this work is to review the main scientific and commercial solutions, and perform automatic sleep staging based on heart rate variability. Results show that, although promising, this approach requires a larger dataset to consider a user-dependent scenario.
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Acknowledgements
This work was supported by the European Regional Development Fund through the programme COMPETE by FCT (Portugal) in the scope of the project PEst-UID/CEC/00027/2015 and Sono ao Volante 2.0 - Information system for predicting sleeping while driving and detecting disorders or chronic sleep deprivation - NORTE-01-0247-FEDER-039720, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement. This research was partially supported by LIACC (FCT/UID/CEC/0027/2020).
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Rodrigues, C., Faria, B.M., Reis, L.P. (2021). Detecting, Predicting, and Preventing Driver Drowsiness with Wrist-Wearable Devices. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_9
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