Key result
A multivariate statistical process control approach using wearable-derived heart rate variability and accelerometer data improved drowsiness transition detection and classification.
Why the study?
Driving when fatigued impairs reaction time and concentration, increasing accident risk, creating a need for an automatic method to detect drowsiness transitions using heart rate variability.
Does an automatic method using heart rate variability and accelerometer data from a wearable device improve the detection of drowsiness transitions?
Does an automatic method using heart rate variability and accelerometer data from a wearable device improve the detection of drowsiness transitions?
An automated method utilizing heart rate variability and accelerometer data from a wearable device can improve the detection of driver drowsiness transitions, though accuracy varies among individuals.
May support wearable drowsiness monitoring in drivers; leaves open prospective validation before any practice change.
Due to a reduction in reaction time and, consequently, the driver’s concentration, driving when fatigued has become an issue throughout time. Consequently, the likelihood of having an accident and it being fatal increases. In this work, we aim to identify an automatic method capable of detecting drowsiness transitions by considering the time, frequency, and nonlinear domains of heart rate variability. Therefore, the methodology proposed considers the multivariate statistical process control, using principal components analysis, with accelerometer and time, frequency, and nonlinear domains of the heart rate variability extracted by a wearable device. Applying the proposed approach, it was possible to improve the results achieved in the previous studies, where it was able to remove points out-of-control due to signal noise, identify the drowsy transitions, and, consequently, improve the drowsiness classification. It is important to note that the out-of-control points of the heart rate variability are not influenced by external noise. In terms of limitations, this method was not able to detect all drowsiness transitions, and in some individuals, it falls far short of expectations. Regarding this, is essential to understand if there is any pattern or similarity among the participants in which it fails.
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Antunes et al. (2023) studied Drowsiness. Multivariate statistical process control using PCA with wearable-derived HRV and accelerometer data was evaluated on Drowsiness transitions detection. A multivariate statistical process control approach using wearable-derived heart rate variability and accelerometer data improved drowsiness transition detection and classification.
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