Supervised ANN-assisted modeling of seated body apparent mass under vertical whole body vibration

Hamid Taghavifar, Subhash Rakheja

Research output: Contribution to journalArticle

8 Citations (Scopus)
17 Downloads (Pure)

Abstract

The modeling of human body response to whole body vibration has been a challenging task owing to the complex dependence on various factors related to anthropometry, and sitting and vibration conditions. This paper addresses the functionality of Artificial Neural Network (ANN) for prediction of seated body apparent mass under different levels of vibration excitations in the 0.5–20 Hz frequency range, while assuming two different sitting postures (with and without a back support). A multilayer feed-forward neural network with back propagation (BP) algorithm was developed with various structures to identify an optimal configuration. From preliminary simulations, a neural network structure with 20 neurons in the first and 20 neurons in the second hidden layers was selected, which resulted in least mean square error, MSE, and highest coefficient of determination R2, when compared to those of the other model structures considered. Portions of the measured data acquired with 51 adult male and female subjects were used in the training and testing phases, which revealed MSE magnitudes of 4.83 and 5.97 kg2, respectively, with R2 values in excess of 0.96. Subsequently, the predicting ability of the model was assessed using the datasets for 14 unforeseen subjects. It was inferred that a well-trained ANN has the capacity to predict biodynamic responses of seated subjects as functions of the body mass, vibration magnitude and support condition. The model could predict the primary resonance frequency and the corresponding magnitude while the validity in predicting the responses were obtained at MSE of 2.13 and 1.83 kg and with R2 values in excess of 0.98 for the male and female subjects, respectively.
Original languageEnglish
Pages (from-to)78-88
Number of pages11
JournalMeasurement
Volume127
Early online date28 May 2018
DOIs
Publication statusPublished - Oct 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Measurement. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Measurement, 127, (2018) DOI: 10.1016/j.measurement.2018.05.092

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Artificial intelligence
  • Seated body biodynamic response
  • Apparent mass
  • Vibration biodynamics

Fingerprint Dive into the research topics of 'Supervised ANN-assisted modeling of seated body apparent mass under vertical whole body vibration'. Together they form a unique fingerprint.

  • Cite this