计算机与现代化Issue(12)：49-55,7.DOI:10.3969/j.issn.1006-2475.2017.12.010
基于SVM_KNN的老人跌倒检测算法
Fall Detection Algorithm Based on SVM_KNN
摘要
Abstract
Falling is one of the main causes of casualties in the elderly,every year about 40 million people over the age of 65 fall accidentally.To improve the accuracy in human fall detection,a fall detection algorithm based on acceleration sensor and barometer in a smart phone is proposed,the algorithm is an improved support vector machine (SVM).Firstly,it uses the SVM to train the training set to obtain a weak 2-classifier (including the optimal hyperplane and support vector set),and then calculates the distance from the sample to the optimal hyperplane.If the distance is greater than the given threshold,the tested sample would be classified with SVM.Otherwise,the K-nearest-neighbor classifier (KNN) method will be used.In addition,in the KNN method,the distance between the eigenvectors is calculated using the standard Euclidean distance.Simulation results show that compared with the non-optimized support vector machine algorithm,this algorithm can effectively improve the fall detection accuracy and smartphones can be placed casually.关键词
跌倒检测/SVM/KNN/SVM_KNN/MatlabKey words
fall detection/SVM/KNN/SVM_KNN/Matlab分类
信息技术与安全科学引用本文复制引用
张舒雅,吴科艳,黄炎子,刘守印..基于SVM_KNN的老人跌倒检测算法[J].计算机与现代化,2017,(12):49-55,7.基金项目
华中师范大学中央高校基本科研业务费教育科学专项资金资助项目(CCNU16JYKX019) （CCNU16JYKX019）
