Providing an Approach for Early Prediction of Fall in Human Activities Based on Wearable Sensor Data and the Use of Deep Learning Algorithms

Abstract

Abstract Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable ‘T’, the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%.