Development and testing a drowsiness detector based on ECG sensors in steering wheel
Abstract
Sleeping or drowsiness while driving contributes to human error, being one of the most relevant causes of traffic collisions and accidents in the world. Although it is foreseen that completely automated vehicles can reduce significantly these numbers, there will be a sequential incorporation of automated vehicles, from existing vehicles, level 0 or 1, to level 2 and level 3 of automation (according to SAE definition) in which the risk of drowsiness can persist or even increase. For this reason, reliable detection of drowsiness is one of the leading objectives in the development of new Advanced Driver Assistance systems. The main hypothesis in the present work is that the physical response in drivers can be indirectly measured via biophysical parameters, such as changes in heart variability (HRV), and that measurement can lead to early drowsiness detection. Following this principle, the main objective of this paper is to present the development a non-invasive system integrated in the vehicle steering wheel to detect the presence of somnolence while driving and validate it via KSS. The final purpose is to integrate the system in vehicles to create warnings or alarms for the driver to avoid accidents related with fatigue or drowsiness.
Keywords: drowsiness, HRV, sensors, steering wheel.
DOI: 10.54941/ahfe1002470
Cite this paper
More from this volume
- When cycling again - Comparison of safety behaviors of between cyclists of shared, private and public bike in China
- SafeBike - a road safety programme for young adolescent cyclists
- Digitizing Buttons: A Comparison of Digital Input Modalities to Replace Physical Buttons in Truck Cockpits
- The Effects of Multi-modal Takeover Request on Distracted Drivers’ Takeover Performance and Perception
- Confidence Horizon for a Dynamic Balance between Drivers and Vehicle Automation: First Sketch and Application
- Meeting User Needs in Vehicle Automation
- Identifying Lane Changes Automatically using the GPS Sensors of Portable Devices
- Driving simulator study for the effects of autonomous vehicles on drivers behaviour under car-following conditions
- Overall effects of non-driving related activities’ characteristics on takeover performance in the context of SAE Level 3: A meta-analysis
- Development of empathic autonomous vehicles through understanding the passenger’s emotional state
- Detection of Discomfort in Autonomous Driving via Stochastic Approximation
- The public requirements on interior facilities of highly automated vehicles in China


AHFE Open Access