# Drowsiness Transitions Detection Using a Wearable Device
## 1 Introduction  
Drowsiness can be caused due to different underlying causes such as excessive daytime drowsiness, an unadjusted work schedule according to the circadian rhythm, sleep deprivation or fatigue, certain medications, and the consumption of alcohol. Furthermore, when an individual does not get a good daily rest, their performance, memory, concentration, reaction times, and alertness will be affected [1, 2]. These effects can present a significant problem when the subject is driving since sleep deprivation affects reaction time, and accidents can occur quickly [3]. Consequentially, this might be fatal for the drivers and/or anybody who cross paths with them. Nevertheless, sleep disorders are considered a public health problem [4]. According to the meta-analysis conducted by [5], it was proved that there is an association between drowsy driving and the risk of a road accident. In addition, it was noted that driving when fatigued increases traffic accidents by a factor of 1.29 to 1.34 compared to driving while not fatigued [5]. Therefore, it is perceptible that drowsiness at the wheel is a public health problem, and it is fundamental to identify appropriate preventive solutions. There are three main categories commonly used to detect drowsiness: behavioral, vehicular, and physiological techniques [6]. Behavioural techniques evaluate the drivers' drowsiness through their behavior. It requires cameras and computer monitoring to extract the most common features, such as eye closure ratio, eye blinking, head position, facial expressions, and yawning. Despite the fact that this technique is noninvasive, some disadvantages can be appointed when the environmental factors are not adequate, for example, the illumination, brightness, and road conditions [7]. Moreover, vehicle techniques are used to observe driving patterns and to detect a decline in drivers' performance due to tiredness and drowsiness taking into account vehicular features. Frequent lane shifts, speed, steeringwheel angle, and grip force collected from the sensors in the steering wheel, accelerator, or brake pedal are among the features that are often employed. This technique can be useful but limited in detecting drowsiness since it can be influenced by external factors, such as road and weather conditions [8]. Finally, because they take physical conditions into account, physiological techniques are the most trustworthy and accurate at detecting tiredness in drivers. Body temperature, heart rate, pulse rate, breathing, and respiratory rate are the most commonly used biological parameters. Another alternative metric used is the heart rate variability, which represents the time difference between successive heartbeats [9]. This metric can be divided into three domains: time, frequency, and nonlinear domains [10]. Succinctly, the time domain quantifies the variability in beat-to-beat interval measurements, whereas the frequency domain is intended to estimate the absolute or relative power distribution of the signal, which is divided into four bands. On the other hand, the nonlinear domain quantifies the unpredictability of a time series [11]. This technique presents a disadvantage in some medical devices, being considered intrusive since electrodes are required on the driver's body and are not always comfortable for the driver's day-to-day life [6]. Even though physiological techniques are reliable, it is commonly used to classify sleep stages, where the ground truth, most of the time, is defined by medical experts or subjective self-evaluation [12]. In order to oppose intrusive devices, for a driver, there are wearable devices, commonly used in the field of healthcare. These devices are used for monitoring and diagnosing the health of the individual, in a more comfortable way and for daily care. Furthermore, it has advantages such as a low cost and the acquisition of data over a long period of time [13]. Numerous studies have been conducted on the detection of drowsiness using wearable devices to monitor a person's health.