As China enters an aging society,addressing safety issues for the elderly population,especially the problem of falling has become increasingly important.A prediction system based on Convolutional Neu-ral Networks(CNN)and multi-feature fusion has been proposed.This system integrates various types of feature information,such as images and physiological signals,to improve the accuracy of falling prediction.Experiments have validated the superiority of the multi-model structure based on CNN in predicting the eld-erly man falling and the enhancement of model performance by the multi-feature fusion strategy.Compared to other methods,the proposed method demonstrates superior performance in terms of accuracy,recall,pre-cision,and F1 score,with an accuracy of 95.93％.This research provides an efficient and reliable method for predicting and preventing falling among the elderly man.
关键词
CNN算法/多特征融合/特征提取/老人摔倒预测/数据集
Key words
CNN algorithm/multi-feature fusion/feature extraction/prediction of elderly man falling/da-ta set
维普
万方数据