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Data Fusion for Deep Learning on Transport Mode Detection: A Case Study

Abstract : In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc. In this domain, the comparisons between each option are not always complete. Experiments on a public, real-life dataset are led here to evaluate carefully each of the choices that were made, with a specific emphasis on data fusion methods. Our most surprising finding is that none of the methods we implemented from the literature is better than a simple late fusion. Two important decisions are the choice of a sensor and the choice of a representation for the data: we found that using 2D convolutions on spectrograms with a logarithmic axis for the frequencies was better than 1-dimensional temporal representations. To foster the research on deep learning with embedded inertial sensors, we release our code along with our publication.
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Submitted on : Thursday, September 30, 2021 - 12:14:38 PM
Last modification on : Sunday, October 3, 2021 - 3:20:21 AM


Data_Fusion_ Deep_Learning _EA...
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Hugues Moreau, Andrea Vassilev, Liming Chen. Data Fusion for Deep Learning on Transport Mode Detection: A Case Study. 22nd Conference on Engineering Applications of Neural Networks, Sep 2021, Online, Greece. ⟨10.1007/978-3-030-80568-5_12⟩. ⟨hal-03242601v3⟩



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