IoT-Based Assessment of a Driver's Stress Level

Area of Science:

• Human-Computer Interaction
• Automotive Safety
• Biomedical Engineering

Background:

• Driver Monitoring Systems (DMSs) are crucial for preventing road accidents by detecting driving anomalies.
• Driver stress and workload can be influenced by traffic and road conditions, impacting psycho-physiological status.
• There is a need for advanced monitoring systems that incorporate psycho-physiological driver aspects.

Purpose of the Study:

• To propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system for assessing driver stress.
• To estimate the driver's arousal level, a key psycho-physiological response to driving tasks.
• To develop an adaptable and effective system for real-time driver monitoring.

Main Methods:

• Utilizing a wearable sensorized bodice and a thermal camera for data collection.
• Extracting physiological parameters such as heart rate and skin temperature.
• Employing innovative algorithms for processing and analyzing collected driver and vehicle data.

Main Results:

• Experimental results were obtained from both simulated and real driving scenarios.
• The proposed system demonstrated adaptability and efficacy in assessing driver stress levels.
• Physiological data successfully correlated with driver arousal during driving tasks.

Conclusions:

• The developed IoT-oriented system effectively monitors driver stress using physiological data.
• The system shows promise for integration into future automotive safety technologies.
• Advanced psycho-physiological monitoring can significantly contribute to preventing hazardous driving events.