A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

作     者：Jyun-Guo Wang 

作者机构：The Department of Medical InformaticsTzu Chi UniversityHualien County97004Taiwan 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2024年第48卷第5期

页      面：1149-1170页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Science and Technology Council under grants NSTC 112-2221-E-320-002 the Buddhist Tzu Chi Medical Foundation in Taiwan under Grant TCMMP 112-02-02 

主　　题：Double interactively recurrent fuzzy cerebellar model articulation controller(D-IRFCMAC) improved particle swarm optimization(IPSO) fall detection 

摘      要：In many Eastern and Western countries,falling birth rates have led to the gradual aging of *** adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous ***,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to *** address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data *** study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection ***,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)***,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)***,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search *** proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data *** UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.