Please use this identifier to cite or link to this item: https://doi.org/10.3390/s19112643
Title: Accurate driver detection exploiting invariant characteristics of smartphone sensors
Authors: Ahn, D.
Park, H. 
Shin, K.
Park, T.
Keywords: Built-in smartphone sensors
Distracted driving
Driver detection
Driving while distracted
Invariant sensory characteristics
Issue Date: 2019
Publisher: MDPI AG
Citation: Ahn, D., Park, H., Shin, K., Park, T. (2019). Accurate driver detection exploiting invariant characteristics of smartphone sensors. Sensors (Switzerland) 19 (11) : 2643. ScholarBank@NUS Repository. https://doi.org/10.3390/s19112643
Rights: Attribution 4.0 International
Abstract: Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver抯 smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption. � 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Sensors (Switzerland)
URI: https://scholarbank.nus.edu.sg/handle/10635/212962
ISSN: 14248220
DOI: 10.3390/s19112643
Rights: Attribution 4.0 International
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