SpiroSonic: monitoring human lung function via acoustic sensing on commodity smartphones
Xingzhe Song, Boyuan Yang, Ge Yang, Ruirong Chen, Erick Forno, Wei Chen, Wei Gao
Abstract
Respiratory diseases have been a significant public health challenge. Efficient disease evaluation and monitoring call for daily spirometry tests, as an effective way of pulmonary function testing, out of clinic. This requirement, however, is hard to be satisfied due to the large size and high costs of current spirometry equipments. In this paper, we present SpiroSonic, a new system design that uses commodity smartphones to support complete, accurate yet reliable spirometry tests in regular home settings with various environmental and human factors. SpiroSonic measures the humans' chest wall motion via acoustic sensing and interprets such motion into lung function indices, based on the clinically validated correlation between them. We implemented SpiroSonic as a smartphone app, and verified SpiroSonic's monitoring error over healthy humans as <3%. Clinical studies further show that SpiroSonic reaches 5%-10% monitoring error among 83 pediatric patients. Given that the error of in-clinic spirometry is usually around 5%, SpiroSonic can be reliably used for disease tracking and evaluation out of clinic.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8346cf61-81cf-4943-9d96-653375c993a9Cited by top-tier papers18
- MoVi-Fi: motion-robust vital signs waveform recovery via deep interpreted RF sensingZhe Chen, Tianyue Zheng, Chao Cai, Jun LuoMobiCom 2021 · 190 citations
- MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel VariationJingzhi Hu, Tianyue Zheng, Zhe Chen, Hongbo Wang et al.MobiCom 2023 · 72 citations
- LaLaRAND: Flexible Layer-by-Layer CPU/GPU Scheduling for Real-Time DNN TasksWoosung Kang, Kilho Lee, Jinkyu Lee, Insik Shin et al.RTSS 2021 · 68 citations
- Fall Detection via Inaudible Acoustic SensingJie Lian, Xu Yuan, Ming Li, Nian-Feng TzengUbiComp 2021 · 48 citations
- MSense: Boosting Wireless Sensing Capability Under Motion InterferenceZhaoxin Chang, Fusang Zhang, Jie Xiong, Weiyan Chen et al.MobiCom 2024 · 40 citations
Builds on1
Related papers
- SpiroSense: Transforming Smartphones into Pulmonary Metrics Monitors with Ultrasonic TechnologyLong Fan, Lei Xie, Shiyuan Ma, Yanling Bu et al.UbiComp 2025 · 3 citations
- EarSpiro: Earphone-based Spirometry for Lung Function AssessmentWentao Xie, Qingyong Hu, Jin Zhang, Qian ZhangUbiComp 2023 · 10 citations
- ESPIRO: Natural Pulmonary Function Monitoring via Earphone-Acquired SpeechYetong Cao, Dong Ma, Wentao Xie, Qian Zhang et al.MobiCom 2025 · 3 citations
- WiPhone: Smartphone-based Respiration Monitoring Using Ambient Reflected WiFi SignalsJinyi Liu, Youwei Zeng, Tao Gu, Leye Wang et al.UbiComp 2021 · 62 citations
- Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze-Sensing Using Mobile SensorsSoujanya Chatterjee, Md. Mahbubur Rahman, Tousif Ahmed, Nazir Saleheen et al.CHI 2020 · 26 citations
