Prediction of a state of a subject on the basis of a stabilogram signal and video oculography test

Abstract

Postural stability decreases with ageing and may lead to accidental falls, isolation and a reduction in the quality of life. The age at the onset of postural derangement, its extent and the reason for deterioration are poorly known within an individual, but in general it becomes more severe with age. In order to prevent falls and avoid severe injuries the postural derangement has to be noticed by the person and the possible nursing personnel. In this work we propose such numerical features, which can discriminate the persons having good or poor postural stability. These features can also be utilized to measure the outcome and progression of balance training. With these postural stability algorithms providing stability features for a subject we managed to classify correctly the type of stance on the force platform in more than 80% of sixty subjects. We used k-nearest neighbor algorithm as an intuitive baseline method and compared its results with those of support vector machines and hidden Markov models.