Abstract
Recognition of human activities aims a wide diversity of applications. However, identifying complicated activities continues a challenging and active research area. In this work, we assess a new approach of feature selection for human activity recognition. For the task, we also compare state-of-the-art classifiers, e.g., Bayes classifier, kNN, MLP, SVM, MLM and MLM-NN. Based on the experiments, the MLM-NN is able to speed up the original MLM while holding equivalent accuracy. MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smartphones.
Keywords
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- 1.
The Harmonic Means (HM) between sensitivity and specificity is computed by \(HM = 2 \cdot \frac{Se \cdot Sp}{Se + Sp}\).
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The first author acknowledge the sponsorship from the Cearense Foundation for the Support of Scientific and Technological Development (FUNCAP) and the National Council for Research and Development (CNPq) by providing financial support.
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Marinho, L.B., de Souza Junior, A.H., Rebouças Filho, P.P. (2017). A New Approach to Human Activity Recognition Using Machine Learning Techniques. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_52
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