Human fall detection algorithm based on random forest and MPU6050
Ziyang Zeng, Kang Xu, Gang He, Hao Feng · 2024
In recent years, the health problems of the aged persons have been paid more and more attention in China. Falls are one of the main reasons affecting the health and safety of the aged, especially accidental injuries caused by falls of the aged persons have attracted much attention. In this paper, the STM32F103C8T6 chip and the MPU6050 sensor are used to collect the posture information of the human body in the normal state and the falling state, and the Fast Fourier Transform was performed on the collected posture data to obtain the frequency data of human behavior. The frequency information is used as the dataset for the random forest model. Five-fold cross-validation strategy was used to increase model performance. Besides, different filtering algorithms were utilized and compared. The model accuracy reached94%, which demonstrated that the proposed algorithm in this paper is promising for better human fall detection problem.