A back-propagation neural network model for prediction of loss of balance

Abstract

Two neural network models were developed for the prediction of postural sway response due to exposure to risk factors including environmental lighting, job-tasks, standing surface firmness, surface oiliness, work load, peripheral vision conditions, age and gender. Variables used to measure the loss of balance were index of proximity to stability boundary and sway length. Tests showed that job-task is the main risk factor that changes the output while there is some impact by age or gender on the outcome of the model. The results from these models can be used to find risk factors that have great impact on loss of balance and therefore can help in designing intervention programs.