Deep learning-based fall detection using commodity Wi-Fi  

作　　者：Tingwei Chen Xiaoyang Li Hang Li Guangxu Zhu 

机构地区：[1]Shenzhen Research Institute of Big Data,Shenzhen 518172,China [2]School of Science and Engineering,The Chinese University of Hong Kong,Shenzhen 518172,China

出　　处：《Journal of Information and Intelligence》2024年第4期355-364,共10页信息与智能学报（英文）

基　　金：supported in part by National Natural Science Foundation of China(62001310,62101235);Guangdong Basic and Applied Basic Research Foundation(2022A1515010109);Shenzhen Science and Technology Program(JCYJ20220530113017039);the internal Project Fund from Shenzhen Research Institute of Big Data(J00120230001,J00220230004).

摘　　要：As the phenomenon of an aging population gradually becomes common worldwide,the pressure on the elderly has seen a notable increase.To address this challenge,fall detection systems are important in ensuring the safety of the elderly population,particularly those living alone.Wi-Fi sensing,as a privacy-preserving method of perception,can be deployed indoors for detecting human activities such as falls,based on the reflective properties of electromagnetic waves.Signals generated by transmitters experience reflections from various objects within indoor environments,leading to distinct propagation paths.These signals eventually aggregate at the receiver,incorporating details about the objects’orientation and their activity states.In this study,within practical experimental environments,we collect dataset and utilize deep learning method to classify the falling events.

关 键 词：Fall detection Wi-Fi sensing Channel state information 

分 类 号：TN91[电子电信—通信与信息系统]