Abstract
Human induced floor vibrations have recently been proposed to track human activity for a number of applications such as health care and surveillance. For example, floor vibrations can be used to identify human falls, or the existence of an intruder in a room. In these applications, the acceleration signals should be classified accurately to eliminate false positives. In this paper, a multi-layer artificial neural network is used to classify floor vibrations. Data from a previously published benchmark problem, which consists of seven types of human activities, is used to train and test the algorithm. Results show the capabilities of a multilayer artificial neural network in human activity classification.
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© 2019 The Society for Experimental Mechanics, Inc.
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Madarshahian, R., Caicedo, J.M., Haerens, N. (2019). Human Activity Benchmark Classification Using Multilayer Artificial Neural Network. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74421-6_27
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DOI: https://doi.org/10.1007/978-3-319-74421-6_27
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