# Smart Steering Wheel Prototype for In-Vehicle Vital Sign Monitoring

## TL;DR

A smart steering wheel prototype was developed to monitor driver vital signs like heart rate without being intrusive, aiming to improve road safety.

## Contribution

The prototype integrates ECG, PPG, and inertial sensors into a steering wheel for non-intrusive driver monitoring.

## Key findings

- The prototype captured high-quality signals suitable for heart rate variability analysis in laboratory settings.
- Real-world testing confirmed the system's functionality despite motion-related noise.
- The device is a feasible solution for unobtrusive driver monitoring and can support machine learning for safety improvements.

## Abstract

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events.