Abstract
Smartphones are present in most people's daily lives. Sensors embedded in these devices open the possibility of monitoring users’ activities. The classification of the intricate data patterns collected through these sensors is a challenging task when considering hand-crafted features and pattern recognition algorithms. In this work, to face this challenge, we propose a convolutional neural network architecture along with two methods for transforming sensor data stream into images. The proposed model was evaluated using the UniMiB SHAR dataset. The best macro average accuracy obtained for classification of 17 types of activities, with fivefold-cross-validation-method, was 90.44%.
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Acknowledgements
This research, carried out within the scope of Samsung-UFAM Project for Education and Research (SUPER), according to Article 48 of Decree nº 6.008/2006(SUFRAMA), was funded by Samsung Electronics of Amazonia Ltda., under the terms of Federal Law nº 8.387/1991, through agreement 001/2020, signed with Federal University of Amazonas and FAEPI, Brazil.
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The authors had declared no interest conflict.
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de Aquino e Aquino, G., Serrão, M.K., Costa, M.G.F., Costa-Filho, C.F.F. (2022). Human Activity Recognition from Accelerometer Data with Convolutional Neural Networks. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_235
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DOI: https://doi.org/10.1007/978-3-030-70601-2_235
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