Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

Sensors (Basel). 2023 Apr 15;23(8):4004. doi: 10.3390/s23084004.


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.

Keywords: active assisted living; driver monitoring; drowsiness detection; galvanic skin response; machine learning; skin conductance; wearable devices.

MeSH terms

  • Algorithms
  • Automobile Driving*
  • Awareness
  • Machine Learning
  • Wakefulness / physiology

Grants and funding

This research was partially funded from the National Funded Programme 2014–2020 under grant agreement n. 1733, (ADAS+ Project). This research was also partially funded by the European Union—Next Generation EU. Project Code: ECS00000041; Project CUP: C43C22000380007; Project Title: Innovation, digitalization and sustainability for the diffused economy in Central Italy—VITALITY. Moreover, this research was also partially funded by the call FAR Mission Oriented 2021 of the University of Modena and Reggio Emilia, Italy, Project CUP: E95F21002890005, Project Title: “SHeMS: a Smart Healthcare Modular System for Personalized Remote Health Monitoring”.