# Drowsiness Transitions Detection Using a Wearable Device
## 6 Conclusions  
Drowsiness at the wheel is a topic of relevance since it reduces the driver's reaction time, and that can lead quickly to road accidents. It can cause injuries or even the deaths of the drivers or those nearby. Therefore, it is important to take preventive measures that are not intrusive for the driver. The aim of this work was the identification of a drowsiness transition method using the heart rate variability and accelerometer information through a wearable device. It is intended to identify the maximum number of drowsiness transitions and to reduce the identification of signal noise through driving simulations. This work contributes to the automatic detection of drowsiness transitions that is not influenced by external subjectivity and noise (from the signal or from large movements). It can be considered an unsupervised methodology and a low-cost system that is nonintrusive for drivers' activity. Driving simulations were developed with 57 people, in which most of them were male, with a normal body mass index, young, and fit. In terms of sleep disorders, a few participants presented a high risk of developing obstructive sleep apnea; they had excessive daytime sleepiness and bad sleep quality. Two interesting remarks are that most of the participants had an accident and had difficulties recognizing obstacles. It was also found that most participants experienced signs of drowsiness, and it was consensual that they occurred mostly in the middle of the night, during the simulation. Therefore, multivariate statistical process control, using principal components analysis, was the method considered. Four main components were considered--the X, Y, and Z values of the accelerometer, time, frequency, and nonlinear domains of the heart rate variability. Then, the out-of-control points reached were analyzed, and it was possible to prove that most of the points of the heart rate variability were not being influenced by the external noise. Besides that, it was also possible to improve the drowsiness classification, previously defined using Wierwille and Ellsworth's drowsiness scale. It was proved that most of the out-of-control points represent drowsiness transitions (83%), and less than half of the drowsiness transitions are detected (47%). Thus, the proposed method showed promising results for detecting drowsiness transitions and is not an intrusive alternative to the driver's work functions. The main limitation of the proposed work is the time consumption that is required since the process of classifying the drowsiness stages is not automatic. Moreover, it was not possible to detect all the drowsiness transitions and, for that reason, the recall value was low. Further analysis is needed to understand why in some participants few transitions are detected. Besides that, machine learning algorithms must be performed to classify the drowsiness state. The identification of the best subset of variables to classify the drowsiness transitions known as feature selection optimization is another type of analysis that could be performed. The implementation in a real context may also be something to consider. A.R.A., conceptualization, methodology, investigation, writing, review, and validation; A.C.B., investigation support, review, validation, and supervision; J.G., investigation support, review, validation, and supervision. All authors have read and agreed to the published version of the manuscript. The project is funded by the \"NORTE-01-0247-FEDER-0039720\", supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). It was also supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of LITEC (protocol code 20220101 and 17 January 2022 date of approval).