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...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.展开更多
基金supported in part by National Natural Science Foundation of China(62001310,62101235)Guangdong Basic and Applied Basic Research Foundation(2022A1515010109)+1 种基金Shenzhen Science and Technology Program(JCYJ20220530113017039)the internal Project Fund from Shenzhen Research Institute of Big Data(J00120230001,J00220230004).
文摘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.
