We present a deep-learning-based approach to maximize the accuracy and reliability of vision-based fall detection and alert systems. e proposed approach utilizes a 3D convolutional neural network (3D-CNN) to analyze the continuous motion data obtained from depth cameras and exploits a data augmentation method to do away with over ing. Our preliminary evaluation results demonstrate that it achieves the classi cation accuracy of up to 96.9%.
Hwang et al. REF proposed a deep learning approach in order to maximize the accuracy of fall detection systems using depth cameras by applying the data augmentation technique and 3D-CNN model.
Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network: Poster Abstract
IoTDI '17
Computer Science