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- Driving simulator for performance monitoring with physiological sensorsPublication . Raimundo, Diogo; Lourenço, André; Abrantes, ArnaldoThis paper describes a driving monitoring system based on a car driving simulator, specifically built for this project, using inexpensive off-the-shelf game technology (software and hardware). The project aims to study the effect of drowsiness (caused by fatigue, alcohol consumption, etc.) in driving performance and how it can be detected in advance in order to mitigate accidents. To achieve this, the system is continuously monitoring driver's physiological signals (e.g., heart rate) as well as her/his driving behavior. Electrocardiography (ECG) signal plays a central role in this project, since a feature derived from it - the HRV (Heart Rate Variability), more concretely its power spectral distribution - is used to continuously estimate the driver's degree of awareness and therefore for generation of alarms. A preliminary evaluation of the proposed system is discussed through the presentation of some experimental results.
- AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhiclesPublication . Esteves, Telma; Pinto, João Ribeiro; Ferreira, Pedro M.; Costa, Pedro Amaro; Rodrigues, Lourenço Abrunhosa; Antunes, Inês; Lopes, Gabriel; gamito, pedro; Abrantes, Arnaldo; Jorge, Pedro; Lourenço, André; Sequeira, Ana F.; Cardoso, Jaime S.; Rebelo, AnaAs technology and artificial intelligence conquer a place under the spotlight in the automotive world, driver drowsiness monitoring systems have sparked much interest as a way to increase safety and avoid sleepiness-related accidents. Such technologies, however, stumble upon the observation that each driver presents a distinct set of behavioral and physiological manifestations of drowsiness, thus rendering its objective assessment a non-trivial process. The AUTOMOTIVE project studied the application of signal processing and machine learning techniques for driver-specific drowsiness detection in smart vehicles, enabled by immersive driving simulators. More broadly, comprehensive research on biometrics using the electrocardiogram (ECG) and face enables the continuous learning of subject-specific models of drowsiness for more efficient monitoring. This paper aims to offer a holistic but comprehensive view of the research and development work conducted for the AUTOMOTIVE project across the various addressed topics and how it ultimately brings us closer to the target of improved driver drowsiness monitoring.