Human Fall Prediction Using Ensemble Learning Technique

Abstract

Accidental falling in the indoor environment during locomotion is very common among aged fellows. This can result in serious injuries or even death if not detected or treated on time. In this work, we applied different supervised machine learning techniques to predict fall or syncope, trained a physiological dataset, and compared their performances with respect to different parameters. Our study shows that ensemble technique gives the best results among the implemented models.