Fall Risk Prediction Using Wireless Sensor Insoles With Machine Learning

Abstract

Accidental fall is a significant health risk among the elderly. However, most of the fall detection systems give notification only after a fall occurs. Therefore, medical attention has shifted to fall preventive measures to reduce risks of fall and prevent any damage entirely. As most fall prediction data in previous literature are obtained from inertial sensors or static pressure sensors, in this study, wireless pressure sensors embedded insoles are used to train machine learning (ML) models to predict the risk of fall of an individual. The novelty of this paper is that dynamic walking data is obtained by wearing smart pressure insoles from 1101 subjects. We applied six different ML models, i.e., support vector machine (SVM), random forest (RF), logistic regression (LR), naive bayes (NB), decision tree (DT), and k-nearest neighbor (kNN). Results show that LR model with oversampling techniques achieved the highest area under curve (AUC) of 0.82, whereas the RF model with oversampling achieved the highest accuracy of 0.81 and specificity of 0.88. The results show that such models combined with pressure embedded wireless sensor insoles are capable for fall risk prediction.