Title: Detection of postural balance degradation using fuzzy neural network

Authors: Neeraj Kumar Singh

Addresses: INPT-ENSEEIHT / IRIT, University of Toulouse, 2 Rue Charles Camichel, BP 7122 31071, Toulouse, Cedex 7, France

Abstract: Postural balance is often studied in order to understand the effect of sensory degradation with age. The aim of this study is to analyse the static and dynamic stabilogram signals to determine different features, which can be used to detect a degradation in equilibrium using the self-adaptive neurofuzzy inference systems (SANFIS). The main features are critical point interval (CPI), autoregressive moving average (ARMA) and area of a curve under the slope (Z-Area) that are identified from the stabilogram signals. The determined features of the stabilogram signals are used to detect and predict the degradation in postural balance using the fuzzy neural network. The selected features are randomly selected for training and testing during the classification and prediction in postural balance, where we have achieved average 95.3% accuracy of the result of classification and prediction of the degradation in equilibrium in 10 trials.

Keywords: centre of pressure; postural control; stepping-up; ground reaction forces; clustering; neuro-fuzzy systems.

DOI: 10.1504/IJBRA.2019.103788

International Journal of Bioinformatics Research and Applications, 2019 Vol.15 No.4, pp.371 - 394

Received: 16 Jul 2016
Accepted: 07 Oct 2017
Published online: 29 Nov 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article