TY - JOUR A1 - Kundinger, Thomas A1 - Yalavarthi, Phani Krishna A1 - Riener, Andreas A1 - Wintersberger, Philipp A1 - Schartmüller, Clemens T1 - Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups JF - International Journal of Pervasive Computing and Communications UR - https://doi.org/10.1108/IJPCC-03-2019-0017 KW - wearable devices KW - physiological measures KW - driver drowsiness detection KW - advanced driver assistance systems (ADAS) KW - simulator study KW - active safety KW - driver monitoring KW - heart rate variability (HRV) KW - machine learning Y1 - 2020 UR - https://doi.org/10.1108/IJPCC-03-2019-0017 SN - 1742-7371 VL - 16 IS - 1 SP - 1 EP - 23 PB - Emerald CY - Bingley ER - TY - JOUR A1 - Göbel, Jan-Philipp A1 - Peuckmann, Niklas A1 - Kundinger, Thomas A1 - Riener, Andreas T1 - Fusion of Driving Behavior and Monitoring System in Scenarios of Driving Under the Influence: An Experimental Approach JF - Applied Sciences N2 - Driving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety. UR - https://doi.org/10.3390/app15105302 Y1 - 2025 UR - https://doi.org/10.3390/app15105302 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59467 SN - 2076-3417 VL - 15 IS - 10 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kundinger, Thomas A1 - Sofra, Nikoletta A1 - Riener, Andreas T1 - Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection JF - Sensors N2 - Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving. UR - https://doi.org/10.3390/s20041029 KW - drowsiness detection KW - driver state KW - simulator study KW - physiological measures KW - machine learning KW - wearable sensors KW - automated driving Y1 - 2020 UR - https://doi.org/10.3390/s20041029 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9506 SN - 1424-8220 VL - 20 IS - 4 PB - MDPI CY - Basel ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta A1 - Weigl, Klemens T1 - Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation T2 - Proceedings: 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications UR - https://doi.org/10.1145/3239060.3239073 KW - Automated Driving KW - driver drowsiness detection KW - driver state KW - simulator study KW - Subjective methods Y1 - 2018 UR - https://doi.org/10.1145/3239060.3239073 SN - 978-1-4503-5946-7 SP - 229 EP - 236 PB - ACM CY - New York ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta A1 - Weigl, Klemens T1 - Driver drowsiness in automated and manual driving: insights from a test track study T2 - IUI 2020: Proceedings of the 25th International Conference on Intelligent User Interfaces UR - https://doi.org/10.1145/3377325.3377506 KW - automated driving KW - driver drowsiness detection KW - driver state KW - field study KW - subjective methods KW - wearables Y1 - 2020 UR - https://doi.org/10.1145/3377325.3377506 SN - 978-1-4503-7118-6 SP - 369 EP - 379 PB - ACM CY - New York ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Sofra, Nikoletta ED - Burghardt, Manuel ED - Wimmer, Raphael ED - Wolff, Christian ED - Womser-Hacker, Christa T1 - A Robust Drowsiness Detection Method based on Vehicle and Driver Vital Data T2 - Mensch und Computer 2017 - Workshopband UR - https://doi.org/10.18420/muc2017-ws09-0307 Y1 - 2017 UR - https://doi.org/10.18420/muc2017-ws09-0307 SP - 433 EP - 440 PB - Gesellschaft für Informatik CY - Regensburg ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas T1 - The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: a Feasibility Analysis T2 - UMAP '20: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization UR - https://doi.org/10.1145/3340631.3394852 KW - drowsiness detection KW - driver state KW - test track study KW - physiological measures KW - machine learning KW - wearable devices KW - automated driving Y1 - 2020 UR - https://doi.org/10.1145/3340631.3394852 SN - 978-1-4503-6861-2 SP - 117 EP - 125 PB - ACM CY - New York ER - TY - CHAP A1 - Kundinger, Thomas A1 - Wintersberger, Philipp A1 - Riener, Andreas T1 - (Over)Trust in Automated Driving: The Sleeping Pill of Tomorrow? T2 - CHI'19 Extended Abstracts: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3290607.3312869 KW - automated driving KW - driver drowsiness KW - trust KW - driver state KW - user study KW - subjective measures Y1 - 2019 UR - https://doi.org/10.1145/3290607.3312869 SN - 978-1-4503-5971-9 PB - ACM CY - New York ER - TY - CHAP A1 - Kundinger, Thomas A1 - Riener, Andreas A1 - Bhat, Ramyashree T1 - Performance and acceptance evaluation of a driver drowsiness detection system based on smart wearables T2 - Proceedings: 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications UR - https://doi.org/10.1145/3409118.3475141 KW - driver drowsiness detection KW - simulator study KW - wearable devices KW - acceptance KW - automated driving KW - prototype KW - machine learning Y1 - 2021 UR - https://doi.org/10.1145/3409118.3475141 SN - 978-1-4503-8063-8 SP - 49 EP - 58 PB - ACM CY - New York ER - TY - JOUR A1 - Kundinger, Thomas A1 - Mayr, Celena A1 - Riener, Andreas T1 - Towards a Reliable Ground Truth for Drowsiness: A Complexity Analysis on the Example of Driver Fatigue JF - Proceedings of the ACM on Human-Computer Interaction UR - https://doi.org/10.1145/3394980 KW - driver drowsiness detection KW - driver state KW - automated driving KW - machine learning KW - subjective measures KW - simulator study Y1 - 2020 UR - https://doi.org/10.1145/3394980 SN - 2573-0142 VL - 4 IS - EICS PB - ACM CY - New York ER -