Enhancing Detection of Falls and Bed-Falls Using a Depth Sensor and Convolutional Neural Network

This study proposes an innovative fall detection system that leverages the capabilities of depth sensors and a convolutional neural network (CNN) model. The objective is to enhance fall detection accuracy by using raw data from depth images for ground reference establishment and to distinguish foreground elements by using a background subtraction algorithm for comprehensive analysis. The performance of the proposed CNN-based system was compared with that of systems based on three learning methods: support vector machine (SVM), multilayer perceptron (MLP), and radial basis function (RBF) neural network models. Experimental results indicated that the proposed system achieved an accuracy rate of approximately 95% and a Kappa coefficient of 0.96 in fall detection, thereby outperforming the other systems. These findings indicate that the proposed system has high efficacy and reliability; thus, it has potential applications in real-world scenarios requiring accurate fall detection.