Why the study?
Traffic accidents caused by drivers falling asleep are a major cause of death, creating a need for effective detection techniques.
Does a real-time PPG-based HRV algorithm accurately predict drowsiness onset in adult subjects?
Does a real-time PPG-based HRV algorithm accurately predict drowsiness onset in adult subjects?
A novel PPG-based algorithm can accurately predict drowsiness onset several minutes before actual sleep, offering a potential physiological-based approach to prevent sleep-related driving accidents.
HRV drowsiness detection may support driver safety monitoring; leaves open need for real-world prospective validation before adoption.
Traffic accidents due to drivers falling asleep while driving is an important cause of death, and effective techniques for coping with this phenomenon are needed. In this paper we present a simple, yet effective algorithm, based on heart rate variability analysis. Results of the experimental validation are reported: two test sessions were performed, one in domestic environments and one in realistic driving environments by using a dynamic driving simulator. The results confirm the results obtained in the previous work (Groppoet al., 2020), and the capability of the proposed algorithm in predicting sleep while driving using a physiological signal that can be collected at the wrist of the driver.
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Pugliese et al. (2022) studied this question.
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