In-vehicle driver health monitoring and alarming system (National Project)

Project Duration: April 2022 – May 2023
Role: Team Leader
Advisor: Prof. Yi Zhang, Southwest Jiaotong University

Project Overview

This national project aimed to develop an advanced in-vehicle driver health monitoring and alarming system to enhance road safety by detecting early signs of fatigue and environmental hazards. The system integrates real-time environmental monitoring and physiological data collection to ensure optimal driving conditions and driver well-being.

The key innovations in this project include:

• Real-time environmental monitoring for maintaining in-vehicle air quality.
• Non-contact ECG signal acquisition using fabric electrodes for fatigue detection.
• Automated alerting mechanism based on deviations from optimal driving conditions.

Project Objectives

1. Develop a real-time monitoring system for detecting environmental and physiological conditions affecting driver alertness.
2. Ensure high-precision data acquisition using non-contact sensor technology.
3. Enhance driver safety by implementing a real-time alerting mechanism.
4. Optimize system performance to ensure reliability and scalability for future applications.

Technical Implementation

1. Environmental Monitoring System

To ensure the in-vehicle environment remains within safe parameters, a real-time monitoring system was implemented:

• Key parameters monitored:
  • Temperature: Maintained within 5% of optimal conditions to prevent thermal discomfort.
  • CO2 levels: Prevented excessive buildup to reduce the risk of drowsiness.
• Wireless data transmission: Sensors transmitted real-time data to an onboard processing unit.
• Automated regulation system: Activated ventilation and air-conditioning adjustments as needed.

2. Non-Contact ECG Signal Acquisition

A non-contact fabric electrode system was developed to continuously monitor driver cardiac activity, enabling early detection of fatigue:

• Fabric electrodes: Integrated into the driver’s seat, ensuring high comfort and non-intrusive signal collection.
• Signal fidelity: Achieved 97% accuracy in ECG signal acquisition.
• Fatigue detection algorithm: Analyzed subtle variations in heart rate and heart rate variability (HRV) to detect drowsiness.

3. Alerting Mechanism

A real-time fatigue and environmental alert system was integrated into the vehicle:

• Multi-modal alarm system: Included audio alerts, seat vibration feedback, and dashboard notifications.
• Machine learning-based anomaly detection: Compared real-time readings against baseline driver health and environmental parameters.
• Intervention threshold tuning: Ensured alerts were triggered only under genuine fatigue or hazardous conditions.

Challenges and Solutions

Key Contributions and Impact

✅ Advanced driver health monitoring: Integrated non-contact ECG and environmental sensors for real-time fatigue detection.
✅ High-accuracy ECG signal acquisition: Achieved 97% signal fidelity, ensuring early and reliable fatigue detection.
✅ Real-time environmental regulation: Maintained in-vehicle conditions within 5% of optimal thresholds.
✅ Scalable architecture: Designed for integration into commercial vehicle fleets for enhanced driver safety.

Conclusion and Future Work

This project successfully developed an innovative driver health monitoring and alarming system, demonstrating its potential for enhancing road safety. Future enhancements include:

• Integrating AI-driven predictive models to anticipate fatigue before it manifests.
• Expanding sensor capabilities to include blood oxygen monitoring and skin temperature tracking.
• Optimizing system miniaturization for commercial vehicle adoption.
• Testing and validation under diverse driving conditions to ensure robustness and adaptability.

This work significantly advances intelligent transportation safety systems, contributing to safer and more reliable driving experiences.