Artificial Neural Network Based Approach for Control Points Detection in Smart Textile Signals
Procedia Computer Science 2017
Artjoms Šutovs, Jurijs Čižovs, Ludmila Aleksejeva, Aleksandrs Okss, Aleksejs Kataševs

This article proposes a method to study the signal coming from the sensors of the smart fabric. At the initial stage, the research is understood as the recognition of so-called control points in the signal, which allow you to get the parameters of an athlete running. This research is part of a larger study which solves the issues of developing a measuring system based on the smart fabric and signal processing research. In turn, the article presents the experimental results of applying the method to detect the control points of the step signal received by the artificial neural network (ANN) from the smart fabric. Within the framework of the method, a conversion of dynamic data into static data is carried out through the fragmentation of the smart fabric signal into training patterns, which are then fed to the ANN. Finally, the trained neural network is able to classify the training patterns, thus allowing one to identify the control points in the run signal in the operation mode. This research is interdisciplinary: it combines textile engineering, data mining and information processes. The research is implemented in collaboration of three institutes of Riga Technical University. The developed methods and results can also be used to study other signals, e.g., those received from sensors of smart medical fabric during the patient rehabilitation period.

Artificial neural network; pattern recognition in signal processing; smart textile signal analysis; smart socks

Šutovs, A., Čižovs, J., Aleksejeva, L., Okss, A., Kataševs, A. Artificial Neural Network Based Approach for Control Points Detection in Smart Textile Signals. Procedia Computer Science, 2017, Vol. 104, pp.548-555. ISSN 1877-0509. Available from: doi:10.1016/j.procs.2017.01.171

Publication language
English (en)
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