Original scientific paper

https://doi.org/10.17559/TV-20220207192647

Artificial Neural Network for Prediction of Seat-to-Head Frequency Response Function During Whole Body Vibrations in the Fore-and-Aft Direction

Slavica Macuzic Saveljic orcid.org/0000-0003-2635-2496 ; Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
Branko Arsic orcid.org/0000-0001-6353-5721 ; Faculty of Science, Department of Mathematics and Informatics, University of Kragujevac Radoja Domanovica 12, 34000 Kragujevac, Serbia; Bioengineering Research and Development Center, Prvoslava Stojanovica 6, 34000 Kragujevac, Serbia
Igor Saveljic orcid.org/0000-0002-0707-5174 ; Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; Bioengineering Research and Development Center, Prvoslava Stojanovica 6, 34000 Kragujevac, Serbia
Jovanka Lukic orcid.org/0000-0002-5893-3976 ; Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
Nenad Filipovic ; Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia

Abstract

Vibrations while driving, regardless of their intensity and shape, have the most obvious effect of reducing driving comfort. Seat-to-head frequency response function (STHT) is a complex relationship resulting from the movement of the head due to the action of excitation on the seat in the form of vibrations in the seat/head interface. In this research, an artificial neural network model was developed, which aims to simulate the STHT function through the body of the subjects based on the data obtained experimentally. The experiments were conducted with twenty healthy male volunteers, who were exposed to single-axis fore-and-aft random broadband vibration. All the results of the experiment were recorded on the basis of which the artificial neural network (ANN) was trained. The developed ANN model has the ability to predict STHT values in the range of trained values both when changing the anthropometric measures of the subjects and changes in the input characteristics of vibrations. The mathematical models based on recurrent neural networks (RNN) used in this paper show with high accuracy STHT values in case there exists prior information about the anthropometric measures of the subjects and the input characteristics of vibrations. The results show that the expensive real-time simulations could be avoided by using reliable neural network models.

Keywords

ANN model; human body response; STHT function; whole body vibration

Hrčak ID:

284916

URI

https://hrcak.srce.hr/284916

Publication date:

29.10.2022.

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