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oai:gredos.usal.es:10366/1351732025-07-31T07:20:43Zcom_10366_122575com_10366_4512com_10366_3823col_10366_156515

Sedano Franco, Javier González Fernández, Silvia Baruque, Bruno Herrero Cosío, Álvaro Corchado Rodríguez, Emilio Santiago 2017-09-06T09:17:16Z 2017-09-06T09:17:16Z 2013 Soft Computing Models in Industrial and Environmental Applications Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing. Volumen 188, pp. 241-248. 978-3-642-32921-0(Print) / 978-3-642-32922-7 (Online) 2194-5357 (Print) / 2194-5365 (Online) http://hdl.handle.net/10366/135173 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. application/pdf en Springer Science + Business Media Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Computer Science Soft Computing for the Analysis of People Movement Classification info:eu-repo/semantics/bookPart