The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness.

The majority of road traffic crashes worldwide are caused by driver drowsiness. For this reason, it is necessary to recognize an incoming drowsiness status for alerting the driver as early as possible, preventing serious accidents. Variation of physiological signals can result from incipient drowsiness that the driver is unaware of, so it is worth investigating if such variation may be exploited for early drowsiness detection, in order to raise a warning. To such an aim, several studies involved mainly bulky and intrusive multimodal acquisition systems to collect driver-related information from several sensors, either worn by the individual and embedded in the car-cabin. The aim of this study is to detect the driver drowsiness through a comfortable wrist-worn device, by analysing only the Skin Conductance (SC) physiological signal. To automatically classify the drowsiness status, three ensemble algorithms have been tested, among which Random Forest results to be the best, featuring an overall accuracy of 84.1%. The obtained results prove that it is possible to classify the drowsy status of a driver from SC signals only, collected on the wrist, and motivates further research aimed at the early identification of the incipient drowsiness, for the implementation of a real-time warning system.

This research introduces the second version of ANGELS, an embedded system designed to analyze PPG and EDA signals in the context of driver monitoring. ANGELS is a cost-effective and energy-efficient solution that performs real-time acquisition and processing of PPG and EDA signals, enabling continuous monitoring of driver physiological parameters. Notably, ANGELS operates autonomously without needing accelerometer data to mitigate distortions caused by vehicle motion. Following an initial validation in collaboration with Maserati, supplementary experiments were conducted within our laboratory-level driving simulator. ANGELS v2 integrates an additional EDA sensor compared to its predecessor. Despite its unobtrusive nature, ANGELS v2 features a mean absolute error of 1.19 BPM in heart rate detection and 1.9 misdetected peaks per minute in EDA peak detection, which is the standard metric to evaluate EDA. These results are achieved within a power envelope of 230 mW. These results underscore the reliability and promising potential of ANGELS v2 to enhance driver safety.