Abstract
Postural Transitions (PTs) are transitory movements that describe the change of state from one static posture to another. In several Human Activity Recognition (HAR) systems, these transitions cannot be disregarded due to their noticeable incidence with respect to the duration of other Basic Activities (BAs). In this work, we propose an online smartphone-based HAR system which deals with the occurrence of postural transitions. If treated properly, the system accuracy improves by avoiding fluctuations in the classifier. The method consists of concurrently exploiting Support Vector Machines (SVMs) and temporal filters of activity probability estimations within a limited time window. We present the benefits of this approach through experiments over a HAR dataset which has been updated with PTs and made publicly available. We also show the new approach performs better than a previous baseline system, where PTs were not taken into account.
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Reyes-Ortiz, JL., Oneto, L., Ghio, A., Samá, A., Anguita, D., Parra, X. (2014). Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_23
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DOI: https://doi.org/10.1007/978-3-319-11179-7_23
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