Abstract
This article presents a study of the best data acquisition conditions regarding movements of extremities in people. By using an accelerometer, there exist different ways of collecting and storing the data captured while people moving. To know which one of these options is the best one, in terms of classification, an empirical study is presented in this paper. As a soft computing technique for validation, Self-Organizing maps have been chosen due to their visualization capability. Empirical verification and comparison of the proposed classification methods are performed in a real domain, where three similar movements in the real-life are analyzed.
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Sedano, J., González, S., Baruque, B., Herrero, Á., Corchado, E. (2013). Soft Computing for the Analysis of People Movement Classification. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_25
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DOI: https://doi.org/10.1007/978-3-642-32922-7_25
Publisher Name: Springer, Berlin, Heidelberg
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