Design of Elderly Fall Detection Based on XGBoost

Abstract

Aging has become a serious problem facing the whole world. Falling is the leading cause of injury and death in the elderly. This paper proposes a fall detection algorithm based on machine learning XGBoost and full-field positioning. Using the data of gyroscope and acceleration sensor, we exploit the “full-field positioning” to increase the dimension of input data and propose a method “maximum satisfaction rate” to mark and train the threshold of data. The experimental results show that this design has obtained high accuracy on falling detection and perfect balance between sensitivity and specificity.