Workshop Title:

Advanced Techniques in Driver Monitoring and Traffic Safety Systems

Date:

June 27th, 2025 (UTC +3)

Organizer:

Department of Biomedical Engineering, Faculty of Engineering, Çukurova University

Keywords:

• Driver Drowsiness Detection
• Physiological Monitoring
• Machine Learning
• Vehicle Safety
• Human-Vehicle Interaction
• Behavioral Analysis

Workshop Chair:

Personal Bio:

Mustafa Istanbullu is an Assistant Professor and vice chair of the undergraduate program in Biomedical Engineering at Çukurova University, where he has been working since 2021. He received his Bachelor's degree in Electrical and Electronics Engineering and later earned a master's degree in Biomedical Engineering from the Istanbul Technical University.

For his master's thesis, he focused on the early diagnosis of osteoporosis using artificial neural networks and support vector machines. He then pursued his PhD at Çukurova University, where his research centered around the design and simulation of a carbon nanotube hybridized field-effect transistor biopotential sensor.

His research interests include microelectronics, microfabrication of semiconductor devices, modeling of electronic devices, analog/digital circuit design, and biomedical instrumentation. He has published articles in international scientific journals and has received an international patent in 2021 for his invention of an ANN-based single calibration impedance measurement system for skin impedance range. He is committed to advancing the field of biomedical engineering and providing his students with an exceptional education.

Workshop Committee Members:

Dr. Mustafa ISTANBULLU, Çukurova University [email protected]

Mr. Mustafa BORAZAN, Çukurova University [email protected]

Workshop Description:

Background:

Driver monitoring systems are essential for enhancing road safety by detecting fatigue and monitoring physiological and behavioral parameters in real-time. Traditional vehicle-based detection methods, such as steering behavior monitoring, offer some insights but are often limited by external factors such as road conditions and weather. The integration of advanced physiological measurements with vehicle-based sensor systems has significantly improved the ability to detect driver fatigue accurately. Recent advancements in biosignal acquisition and AI-driven real-time processing have enabled more precise and adaptive monitoring solutions.

Goal/Rationale:

The goal of this workshop is to present cutting-edge techniques in driver monitoring, with a strong technical focus on the engineering aspects of physiological measurements and artificial intelligence-driven fatigue assessment. The proposed system integrates multiple data sources for real-time monitoring, including:

• Infrared camera-based detection of body temperature, blinking frequency, and yawning.
• Steering wheel sensor-based measurement of galvanic skin response (GSR), electrocardiogram (ECG), and heart rate (HR).
• Analog-to-Digital Conversion (ADC) and microcontroller-based (MCU) signal processing for data acquisition.
• Filtering techniques to enhance signal clarity and reduce noise.
• Artificial intelligence-based fatigue level assessment, leveraging machine learning algorithms for real-time driver state classification.
• Integrated display system to provide real-time feedback on driver fatigue levels.

This workshop aims to provide participants with a deeper understanding of real-time physiological monitoring techniques and their direct integration into vehicle systems with AI-driven decision-making.

Scope and Information for Participants:

This workshop is designed for researchers, engineers, and professionals working in biomedical engineering, artificial intelligence, embedded systems, and human-machine interaction. Topics covered include:

• Physiological-based detection techniques (ECG, GSR, infrared imaging)
• Behavioral-based detection methods (eye tracking, facial expressions, body temperature changes)
• Vehicle-based detection techniques (steering wheel sensors)
• Artificial intelligence and embedded system integration for real-time driver monitoring
• Machine learning models for fatigue level determination
• Practical applications, industry case studies, and future research directions

Participants will gain an in-depth understanding of cutting-edge driver monitoring technologies and contribute to discussions on how these solutions can be effectively integrated into vehicle systems. The workshop will include interactive presentations, hands-on demonstrations, and a panel discussion with industry experts to ensure a comprehensive learning experience.

Venue:

Balcalı, Çukurova Üniversitesi Rektörlüğü, 01330 Sarıçam/Adana, Turkey

VISA:

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