Fall Detection Using Convolutional Neural Network With Multi-Sensor Fusion

Abstract

In this paper, a fall detection method is proposed by employing deep learning and multi-sensors fusion. Continuous wave radar and optical cameras are used simultaneously to capture human action information. Based on the abstraction of both the microwave and optical characteristics of the captured information, multiple convolutional neural network (CNN) is used to realize the information training and fall action recognition. Due to the fusion of multi-sensor information, the overall performance of the fall detection system can be improved remarkably. Detailed experiments are given to validate the proposed method.