Transportation mode detection from smartphone data is investigated as a relevant problem in the multi-modal transportation systems context. Neural networks are chosen as a timely and viable solution. The goal of this paper is to solve such a problem with a combination model of Convolutional Neural Network (CNN) and Bidirectional-Long short-term memory (BiLSTM) only processing accelerometer and magnetometer data. The performance in terms of accuracy and F1 score on the Sussex-Huawei Locomotion-Transportation (SHL) challenge 2018 dataset is comparable to methods that require the processing of a wider range of sensors. The uniqueness of our work is the light architecture requiring less computational resources for training and consequently a shorter inference time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep CNN-BiLSTM Model for Transportation Mode Detection Using Smartphone Accelerometer and Magnetometer


    Beteiligte:
    Tang, Qinrui (Autor:in) / Jahan, Kanwal (Autor:in) / Roth, Michael (Autor:in)


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    1809902 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep CNN-BiLSTM Model for Transportation Mode Detection Using Smartphone Accelerometer and Magnetometer

    Tang, Qinrui / Jahan, Kanwal / Roth, Michael | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2022

    Freier Zugriff

    A Fiber Optic Accelerometer—Magnetometer

    Wang, Z. / Zhang, W. / Huang, W. et al. | British Library Online Contents | 2017


    A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data

    Liang, Xiaoyuan / Zhang, Yuchuan / Wang, Guiling et al. | IEEE | 2020


    Speed Estimation using Smartphone Accelerometer Data

    Ustun, Ilyas / Cetin, Mecit | Transportation Research Record | 2019