Current interior sensing systems already enable the detection of critical driver states such as drowsiness or inattention. In order to extend the system’s capabilities, this work firstly investigates a possible detection of driver fainting via an interior sensing camera. An approach that supports the simulation of driver fainting is developed and realized in a parked vehicle as well as during manual and automated driving, with 61 participants in total. Moreover, multiple instructed intentional movements with for- and side-ward movements of the body are recorded. Classification models are developed based on features that are derived from head and body pose data. These models are then applied to the complete video streams that include various waiting and driving scenarios. The best classification results are seen with Random Forest classifiers with up to 84% true positive detections and 0.33 false positive detections per hour. The majority of false positive detections were seen during automated driving. Implications and options for future research are discussed.

In order to detect a person's individual momentary thermal sensation and comfort, an increased number of models are developed that involve physiological data, especially skin temperatures and features based on the heart activity. In this paper, we investigate the feasibility of Machine Learning (ML) models which include physiological data based on two data sources: (1) a smartwatch and (2) a portable chest belt device. Further, we investigate the difference between thermal sensation and comfort votes and propose a new combined ground truth label. Data were collected in lab-like studies in a single-bed hotel room. Our study focuses on the detection of cold-induced thermal discomfort by varying the room air temperature between 24 °C and 20 °C. Results show that ML based approaches lead to accuracies up to 83.1% when using a chest belt device. Using a smartwatch, accuracies drop down to 79.8%. Our investigation shows that even though Heart Rate (HR) based features improve the prediction accuracy, the highest feature importances have distal skin temperature features.