Fall Risk Prediction Method Based on Human Electrostatic Field and Stacking Ensemble Learning Algorithm

Accurate fall risk prediction is crucial for early intervention and prevention, effectively reducing the incidence of falls and the associated harm. This paper proposes a non-contact gait detection and fall risk prediction method based on the human electrostatic field and Stacking ensemble learning algorithm. A theoretical model for gait detection based on the human electrostatic field is established, and an experimental scheme is designed. The electrostatic gait measurement system is used to collect electrostatic gait signals from healthy young individuals, healthy elderly individuals, and elderly individuals with a history of falls. Gait features, including 28-dimensional quantifiable characteristics, are proposed for evaluating human balance and motor abilities, covering four aspects: gait time parameters, gait symmetry based on ratios and signal similarity, gait stability based on the maximum Lyapunov exponent and entropy information, and gait time parameter variability. A hybrid feature reduction method based on Particle Swarm Optimization (PSO) is used to obtain the optimal feature subset. Fall risk prediction models based on single classifiers (DT, SVM, KNN, and NB) are constructed using both the original feature set and the optimal feature subset. The single classifier based on the optimal feature subset achieves better classification performance. Furthermore, a Stacking ensemble learning model using LightGBM as the meta-learner is developed, achieving an accuracy of 97.78%. This study provides a novel approach for fall risk prediction that can predict the likelihood of falls and reduce the probability of their occurrence.