Kaiyi Guo (shanghai jiao tong university, shanghai, China)Qian Zhang (Shanghai Jiao Tong University, Shanghai, China)Dong Wang (Shanghai Jiao Tong University, Shanghai, China)
Monitoring the occurrence count of abnormal respiratory symptoms helps provide critical support for respiratory health. While this is necessary, there is still a lack of an unobtrusive and reliable way that can be effectively used in real-world settings. In this paper, we present EchoBreath, a passive and active acoustic combined sensing system for abnormal respiratory symptoms monitoring. EchoBreath novelly uses the speaker and microphone under the frame of the glasses to emit ultrasonic waves and capture both passive sounds and echo profiles, which can effectively distinguish between subject-aware behaviors and background noise. Furthermore, A lightweight neural network with the 'Null' class and open-set filtering mechanisms substantially improves real-world applicability by eliminating unrelated activity. Our experiments, involving 25 participants, demonstrate that EchoBreath can recognize 6 typical respiratory symptoms in a laboratory setting with an accuracy of 93.1%. Additionally, an in-the-semi-wild study with 10 participants further validates that EchoBreath can continuously monitor respiratory abnormalities under real-world conditions. We believe that EchoBreath can serve as an unobtrusive and reliable way to monitor abnormal respiratory symptoms.