Artificial neural network models in the diagnosis of balance impairments

Abstract

Falls in older population is a major public health issue and tripping is a major cause of falls. The main aim of this research is to explore effectiveness of artificial neural network (ANN) models for automated recognition of gait changes due to falling behaviour. Minimum foot clearance (MFC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with reported balance problem and tripping falls history. MFC histogram characteristic features were used as inputs to a three-layer ANN model with back-propagation error corrections algorithm to build relationships between MFC features and healthy/balance-impaired category. A number of tests were performed to fin..