LAUSR

Objective: For driver state estimation, physiological features might be promising input parameters. As cable-bound sensing of these parameters is impractical for ubiquitous monitoring, the measurement certainly has to be based on unobtrusive and contact-free technologies. In this work, unobtrusive methods for heart rate (HR) and respiration rate (RR) monitoring, including a hybrid imaging approach, are evaluated under simulated and real driving conditions. Methods: The feasability of unobtrusive methods was tested by comparing measurements from unobtrusive sensors to reference sensors. Under laboratory conditions, magnetic induction and photoplethysmography, both integrated into the seat belt, and hybrid imaging, combining visual and thermal imaging, were evaluated for RR sensing. In real driving, creating an urban and a rural scenario, sensing of RR by hybrid imaging and sensing of HR by a seat-integrated capacitive ECG were evaluated. Results: Under laboratory conditions, a reliable RR detection was possibly using all three sensor technologies. In real-world driving, a reliable HR and RR detection was possible during the rural scenario. In the urban scenario, only the RR detection was feasible. Due to motion artifacts, the capacitive ECG was disturbed and the HR detection impaired. Conclusion: The evaluated unobtrusive measurement systems can monitor physiological parameters during e.g. long-time driving on highways, but may not yet be feasible for monitoring during agile inner-city driving situations, due to motion artifacts. Therefore, future work should focus on artifact reduction. Significance: Physiological features might be used as input parameters for driver state estimation systems. This work presents unobtrusive sensing methods for these parameters.