@inproceedings{7cd44d92a37c44f6ba91d707c8b4f74a,
title = "Detecting drivers smartphone: A learned features approach using aggregated scalogram images",
abstract = "In this paper, we propose an image representation approach for detecting driver mobile phone from the accelerometer signals produced by a set of smartphones in a vehicle. Rather than following the classic paradigm of classifying the signal as driver or non-driver, we propose an original paradigm whereby we aggregate the signals together and train a classifier to detect the driver signal in that aggregation. We do so by stacking-up the Scalograms images of the smartphone signals and training a CNN classifier to identify the driver's Scalograms instance in the Scalograms stack image. To the best our knowledge, this is the first time such an image-fusion and classification scheme is proposed for detecting driver's smartphone. Experiments performed with an in-house dataset confirms the potential and the merit of our approach.",
keywords = "CNN classification, Image fusion, Image representation, Learned features, Smartphone Sensing",
author = "Mohammad Madine and Ammar Battah and Aaminah Khan and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
doi = "10.1109/AICCSA47632.2019.9035262",
language = "British English",
series = "Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA",
publisher = "IEEE Computer Society",
booktitle = "16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019",
address = "United States",
}