Fall Detection System Based on Millimeter Wave Radar and Machine Learning

Abstract

Nowadays, falling is a growing threat to the elderly. This paper combines millimeter wave radar technology, machine learning algorithm, wireless communication technique and cloud platform to realize a fall detection system. In this project, the millimeter wave radar is used to sample the human posture point cloud data, and we create a data set that consist of point of clouds of two different human poses. Random forest and BP neural network are used to train the fall detection model. The system will send the human posture point data to the trained model and realize the fall detection. Besides, the system will use 4G communication technology to transform the data to the web cloud platform. This web page serves as an warning function, which can report the acceleration, speed and other information. According to our experiments, the millimeter wave radar system that we built in this paper can effectively detect human point cloud, and can send human point cloud data packets to the recognition model to detect human falls. In machine learning part, both Random forest and BP neural network models show very strong robustness after repeated adjusting parameters. Random forest has the advantages of light weight and interpretability, which can reach 93% recognition accuracy. The recognition accuracy of BP neural network is even up to 95%, which is higher than other detection models in previous works. Besides, BP neural network model also has higher recall rate in the categories of human falls, which fully meets the requirements of this project.