Data Fusion for Deep Learning on Transport Mode Detection: A Case Study
Résumé
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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TMD_case_studyEANN_preprint/main.pdf (443.08 Ko)
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TMD_case_studyEANN_preprint/Images/average_spectrum_short.png (78.22 Ko)
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TMD_case_studyEANN_preprint/Images/preprocessing_illustration/spectro.PNG (38.52 Ko)
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TMD_case_studyEANN_preprint/Images/preprocessing_illustration/spectro_logfreq.PNG (27.41 Ko)
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TMD_case_studyEANN_preprint/Images/preprocessing_illustration/spectro_logfreq_logpow.PNG (27.02 Ko)
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TMD_case_studyEANN_preprint/Images/preprocessing_illustration/temporal.PNG (18.12 Ko)
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TMD_case_studyEANN_preprint/main.log (18.13 Ko)
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TMD_case_studyEANN_preprint/main.synctex.gz (118.91 Ko)
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