# Drowsiness Transitions Detection Using a Wearable Device
## 1 Introduction
Note that most of the studies performed driver simulations to collect biometric data in a controlled environment. The classification of drowsiness level, in most of the studies, is based on blinks metrics, head pose, and subjective measures that evaluate the level of drowsiness based on predefined classifications with different indicators to be observed for each level of drowsiness [14,15]. Different scales can be applied for rating the level of drowsiness by videoing the drives' faces, such as the Wierwille and Ellsworth's drowsiness scale [16], Stanford Sleepiness Scale [17], and Observer Rating of Drowsiness [18]. The major drawback of these scales is that, since they rely on the rating person's judgment, they can be quite subjective. A new approach was developed to detect drowsiness using an anomaly detection known as multivariate statistical process control. Only one principal component was applied with eight features derived from the heart rate variability [19]. Then, an improvement was proposed in [20] to detect more drowsiness periods. Instead of using only one principal component, three components were considered and designated by time, frequency, and nonlinear domains of the heart rate variability. However, it was not possible to prove the influence of the signal noise on the drowsiness detection [20]. The following step of that analysis was to rate the drowsiness, of each participant, using the recorded video, considering [PERSON] [PERSON]'s drowsiness scale [16]. Heart rate variability and electroencephalogram (EEG) metrics were analyzed and the multivariate statistical process control was applied for both metrics. Compared to the drowsiness levels it was possible to prove that the out-of-control points represent the drowsiness transitions. Moreover, the methodology presents promising results using the heart rate variability since it detects more drowsiness transitions than EEG metrics [21]. Nonetheless, the signal noise was not evaluated. This work intends to identify a suited method capable of recognizing automatically the transitions of the drowsiness phases. This method takes into consideration the heart rate variability metrics (time, frequency, and nonlinear domains) and the accelerometer information, retrieved from the Empatica E4 wristband device. In order to achieve the goal of the proposed work, Multivariate Statistical Process Control, considering Principal Component Analysis (MSPC-PCA) was applied. This methodology is used to improve the results achieved in [20,21], with the addition of accelerometer information used to reduce signal noise and, consequently, identify the maximum number of drowsiness transitions. The main contribution of this paper is the proposal of a method that can identify drowsiness transitions automatically as opposed to manually labelling them or utilizing the classifications of polysomnography tests made by technical specialists. Being an automatic method, it is not prone to subjectivity because it does not depend on the analyst and does not require medical professionals. Thus, it can be considered as an unsupervised approach, using a low-cost wearable device that is nonintrusive. Nevertheless, the challenge of using wearable devices is the noise present in the signal that sometimes influences drowsiness detection. With the proposed method, this issue can be overcome since the detection of drowsiness transitions is not influenced by signal noise. This article is structured as follows. In Section 2, we present the materials and methods, where the driving simulation and analysis procedure, implementation details, and the description of the participants are explained. In Section 3, the variables description and the main results are presented, with the characterization of the proposed methodology to improve the drowsiness classification. The discussion of the results is presented in Section 4, and the main conclusions are reported in Section 5.