Investigating the Impact of Activity Class Number in Fall Detection Systems

Abstract

In this study, the contribution of reducing the number of classes in the machine learning based classifiers to the accuracy level of the fall detection systems developed for older individuals is investigated. Within the scope of the study, a public dataset is used and the appropriate data source is determined, the features frequently used in determining the activity and posture are extracted and the analysis of the most successful classifier is made according to the machine learning metrics using all the classifiers in the MATLAB Machine Learning Framework. The number of classes is gradually decreased and its effect on success is examined. The classification carried out with the Cubic Support Vector Machine (Cubic SVM) algorithm, which is determined as the most successful classifier in the classification without reducing the number of classes at the initial stage, is 96.4%. In accordance with the nature of the problem, contrary to the studies in the literature, when the number of classes is reduced to two, the falls are correctly determined by 99.3% by using k nearest neighbor algorithm.