Fine-grained sleep monitoring: Hearing your breathing with smartphones
Abstract
Sleep monitoring has drawn increasingly attention as the quality and quantity of the sleep are important to maintain a person's health and well-being. For example, inadequate and irregular sleep are usually associated with serious health problems such as fatigue, depression and cardiovascular disease. Traditional sleep monitoring systems, such as PSG, involve wearable sensors with professional installations, and thus are limited to clinical usage. Recent work in using smartphone sensors for sleep monitoring can detect several events related to sleep, such as body movement, cough and snore. Such coarse-grained sleep monitoring however is unable to detect the breathing rate which is an important vital sign and health indicator. This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones. Our system exploits the readily available smartphone earphone placed close to the user to reliably capture the human breathing sound. Given the captured acoustic sound, our system performs noise reduction to remove environmental noise and then identifies the breathing rate based on the signal envelope detection. Our system can further detect detailed sleep events including snore, cough, turn over and get up based on the acoustic features extracted from the acoustic sound. Our experimental evaluation of six subjects over six months time period demonstrates that the breathing rate monitoring and sleep events detection are highly accurate and robust under various environments. By combining breathing rate and sleep events, our system can provide continuous and noninvasive fine-grained sleep monitoring for healthcare related applications, such as sleep apnea monitoring as evidenced by our experimental study.
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Breathing rate via smartphones for sleep monitoring detects sleep events including snore, cough, turn over, and get up.
“another study explored the potential of breathing rate by leveraging smartphones for sleep monitoring, which detects sleep events including snore, cough, turn over and get up [30].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [30]
Cites
Kushida, C. A., Littner, M. R., Morgenthaler, T., Alessi, C. A., Bailey, D., Coleman, J., Friedman, L., Hirshkowitz, M., Kapen, S., Kramer, M., et al. (2005) · Sleep · doi:10.1093/sleep/28.4.499 · Cited by 2,054 · see citing works
Unobtrusive Sleep Monitoring using Smartphones
Chen, Z., Lin, M., Chen, F., Lane, N., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., & Cambell, A. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.252148 · Cited by 300 · see citing works
Self-citationSleep-disordered breathing and type 2 diabetes
Shaw, J. E., Punjabi, N. M., Wilding, J. P. H., Alberti, K. G. M. M., & Zimmet, P. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.04.025 · Cited by 296 · see citing works
Sleep-Related Problems in Common Medical Conditions
Parish, J. M. (2009) · CHEST Journal · doi:10.1378/chest.08-0934 · Cited by 240 · see citing works
iSleep: unobtrusive sleep quality monitoring using smartphones
Hao, T., Xing, G., & Zhou, G. (2013) · Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2517351.2517359 · Cited by 216 · see citing works
Spectral characteristics of normal breath sounds
Gavriely, N., Palti, Y., & Alroy, G. G. (1981) · Journal of Applied Physiology · doi:10.1152/jappl.1981.50.2.307 · Cited by 190 · see citing works
Monitoring Breathing via Signal Strength in Wireless Networks
Patwari, N., Wilson, J., Ananthanarayanan, S., Kasera, S. K., & Westenskow, D. R. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2013.117 · Cited by 128 · see citing works
Automatic assessment of problem behavior in individuals with developmental disabilities
Plötz, T., Hammerla, N., Rozga, A., Reavis, A. R., Call, N. A., & Abowd, G. D. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370276 · Cited by 127 · see citing works
Deleterious Effects of Sleep-Disordered Breathing on the Heart and Vascular System
Dincer, H. E., & O’Neill, W. (2005) · Respiration · doi:10.1159/000089814 · Cited by 109 · see citing works
Acoustic Feature Combination for Robust Speech Recognition
Zolnay, A. G., Schlueter, R., & Ney, H. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415149 · Cited by 73 · see citing works
Braun, P. X., Gmachl, C. F., & Dweik, R. A. (2012) · IEEE Sensors Journal · doi:10.1109/jsen.2012.2210403 · Cited by 49 · see citing works
Dijk, E. V. D. W.-V., Hilgenkamp, T. I. M., Evenhuis, H. M., & Echteld, M. A. (2011) · Journal of Intellectual Disability Research · doi:10.1111/j.1365-2788.2011.01458.x · Cited by 24 · see citing works
REM Sleep Classification with Respiration Rates
Chung, G. S., Choi, B. H., Kim, K. K., Lim, Y. G., Choi, J. W., Jeong, D., & Park, K. S. (2007) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2007.4407379 · Cited by 20 · see citing works
Takatori, F., Yamamori, S., Inoue, M., Abe, S., & Miyasaka, K. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090279 · Cited by 17 · see citing works
Comparison of flow-sound relationship for different features of tracheal sound
Yadollahi, A., & Moussavi, Z. (2008) · doi:10.1109/iembs.2008.4649276 · Cited by 13 · see citing works
Flagged first, then by citations
1 of the 16 references couldn’t be matched to a known paper.
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Wang, X., Yang, C., & Mao, S. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.206 · Cited by 369 · see citing works
Monitoring Vital Signs and Postures During Sleep Using WiFi Signals
Liu, J., Chen, Y. J., Wang, Y., Chen, X., Cheng, J., & Yang, J. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2822818 · Cited by 242 · see citing works
Self-citationZeng, Y., Wu, D., Gao, R., Gu, T., & Zhang, D. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264958 · Cited by 225 · see citing works
Wang, X., Yang, C., & Mao, S. (2017) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3078855 · Cited by 200 · see citing works
Wiar: A Public Dataset for Wifi-Based Activity Recognition
Guo, L., Guo, S., Wang, L., Lin, C., Liu, J., Lu, B., Fang, J., Liu, Z., Shan, Z., & Yang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2947024 · Cited by 131 · see citing works
Self-citationXu, X., Yu, J., Chen, Y. J., Zhu, Y., Kong, L., & Li, M. (2019) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326074 · Cited by 129 · see citing works
Self-citationDevices for home detection of obstructive sleep apnea: A review
Mendonça, F., Mostafa, S. S., Ravelo-García, A. G., Morgado‐Dias, F., & Penzel, T. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2018.02.004 · Cited by 126 · see citing works
Acousticcardiogram: Monitoring Heartbeats using Acoustic Signals on Smart Devices
Qian, K., Wu, C., Xiao, F., Zheng, Y., Zhang, Y., Yang, Z., & Liu, Y. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8485978 · Cited by 122 · see citing works
A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future Trends
Yang, Y., Wang, H., Jiang, R., Guo, X., Cheng, J., & Chen, Y. J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3144400 · Cited by 126 · see citing works
Self-citationShih, C.-H. I., Tomita, N., Lukic, Y. X., Reguera, Á. H., Fleisch, E., & Kowatsch, T. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369835 · Cited by 104 · see citing works
Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey
Cai, C., Zheng, R., & Luo, J. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3145856 · Cited by 103 · see citing works
Acoustic-based sensing and applications: A survey
Bai, Y., Lü, L., Cheng, J., Liu, J., Chen, Y. J., & Yu, J. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107447 · Cited by 99 · see citing works
Self-citationSun, X., Li, Q., Wu, Y., Tang, Y., & Cao, G. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130969 · Cited by 91 · see citing works
Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices
Wang, T., Zhang, D., Wang, L., Zheng, Y., Gu, T., Dorizzi, B., & Zhou, X. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2877607 · Cited by 90 · see citing works
Continuous and fine-grained breathing volume monitoring from afar using wireless signals
Nguyen, P., Zhang, X., Halbower, A. C., & Vu, T. N. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524402 · Cited by 85 · see citing works
Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data
Han, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., & Cecilia Mascolo, C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414576 · Cited by 80 · see citing works
TagSheet: Sleeping Posture Recognition with an Unobtrusive Passive Tag Matrix
Liu, J., Chen, X., Chen, S., Liu, X., Wang, Y., & Chen, L. (2019) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2019.8737599 · Cited by 77 · see citing works
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
Cuttone, A., Bækgaard, P., Sekara, V., Jonsson, H., Larsen, J. E., & Lehmann, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169901 · Cited by 66 · see citing works
Ma, D., Ferlini, A., & Cecilia Mascolo, C. (2021) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3458864.3467680 · Cited by 62 · see citing works
Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors
Akmandor, A. O., Yin, H., & Jha, N. K. (2018) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2018.2864297 · Cited by 59 · see citing works
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Wang, Z., Jiang, K., Hou, Y., Huang, Z., Dou, W., Zhang, C., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2922244 · Cited by 58 · see citing works
Hand Gesture Recognition Based on Active Ultrasonic Sensing of Smartphone: A Survey
Wang, Z., Hou, Y., Jiang, K., Dou, W., Zhang, C., Huang, Z., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2933987 · Cited by 57 · see citing works
Accurate Ambient Noise Assessment Using Smartphones
Zamora, W., Calafate, C. T., Cano, J., & Manzoni, P. (2017) · Sensors · doi:10.3390/s17040917 · Cited by 55 · see citing works
A Survey of Sensors in Healthcare Workflow Monitoring
Antunes, R. S., Seewald, L. A., Rodrigues, V. F., Costa, C. A. D., Jr., L. G., Righi, R. R., Maier, A., Eskofier, B., Ollenschläger, M., Naderi, F., et al. (2019) · ACM Computing Surveys · doi:10.1145/3177852 · Cited by 48 · see citing works
TensorBeat: Tensor Decomposition for Monitoring Multi-Person Breathing Beats with Commodity WiFiPreprint
Wang, X., Yang, C., & Mao, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.02046 · Cited by 52 · see citing works
Liu, Q., Huang, J., & Zhou, Z. (2020) · Children and Youth Services Review · doi:10.1016/j.childyouth.2020.105590 · Cited by 54 · see citing works
Ubiquitous Smartphone-Based Respiration Sensing With Wi-Fi Signal
Yin, Y., Xu, Y., Xiong, J., Lee, S. I., Chen, P., & Niu, Q. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088338 · Cited by 47 · see citing works
SonarBeat: Sonar Phase for Breathing Beat Monitoring with Smartphones
Wang, X., Huang, R., & Mao, S. (2017) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2017.8038412 · Cited by 40 · see citing works
Romano, C., Nicolò, A., Innocenti, L., Bravi, M., Miccinilli, S., Sterzi, S., Sacchetti, M., Schena, E., & Massaroni, C. (2023) · Biosensors · doi:10.3390/bios13060637 · Cited by 39 · see citing works
Cay, G., Ravichandran, V., Sadhu, S., Zisk, A. H., Salisbury, A. L., Solanki, D., & Mankodiya, K. (2022) · IEEE Access · doi:10.1109/access.2022.3210518 · Cited by 44 · see citing works
Chen, X., Xiao, Y., Tang, Y., Fernández‐Mendoza, J., & Cao, G. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3463514 · Cited by 39 · see citing works
Learning to Recognize Handwriting Input with Acoustic Features
Yin, H., Zhou, A., Su, G., Chen, B., Liu, L., & Ma, H. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3397334 · Cited by 37 · see citing works
Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones
Kumar, A., Mitra, V., Oliver, C., Ullal, A. V., Biddulph, M., & Mance, I. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629661 · Cited by 38 · see citing works
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Ren, Y., Wang, C., Chen, Y. J., Yang, J., & Li, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2922283 · Cited by 35 · see citing works
Self-citationMosleh, S., Coder, J. B., Scully, C. G., Forsyth, K. W., & Kalaa, M. O. A. (2022) · IEEE Access · doi:10.1109/access.2022.3230003 · Cited by 34 · see citing works
A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach
Jeng, P.-Y., Wang, L., Hu, C., & Wu, D. (2021) · Sensors · doi:10.3390/s21010258 · Cited by 34 · see citing works
Sleep stages classification by CW Doppler radar using bagged trees algorithm
Zhang, L., Xiong, J., Zhao, H., Hong, H., Zhu, X., & Li, C. (2017) · IEEE Radar Conference (RadarConf) · doi:10.1109/radar.2017.7944310 · Cited by 33 · see citing works
Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey
Tabatabaei, S. A. H., Fischer, P., Schneider, H., Koehler, U., Groß, V., & Sohrabi, K. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3002970 · Cited by 32 · see citing works
Lukic, Y. X., Teepe, G. W., Fleisch, E., & Kowatsch, T. (2022) · JMIR Serious Games · doi:10.2196/39186 · Cited by 32 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
Vibration sensing-based human and infrastructure safety/health monitoring: A survey
Valero, M., Li, F., Zhao, L., Zhang, C., Garrido, J. M., & Han, Z. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103037 · Cited by 28 · see citing works
RF-RVM: Continuous Respiratory Volume Monitoring With COTS RFID Tags
Chang, X., Dai, J., Zhang, Z., Zhu, K., & Xing, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3063718 · Cited by 27 · see citing works
Sleep apnea detection via smart phones
Camcı, B., Kahveci, A. Y., Bert Arnrich, A., & Ersoy, C. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960484 · Cited by 26 · see citing works
A Survey of Approaches to Unobtrusive Sensing of Humans
Fernandes, J. M., Silva, J. S., Rodrigues, A., & Boavida, F. (2022) · ACM Computing Surveys · doi:10.1145/3491208 · Cited by 24 · see citing works
Zhang, S., Nemati, E., Dinh, M., Folkman, N., Ahmed, T., Rahman, M., Kuang, J., Alshurafa, N., & Gao, A. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746334 · Cited by 23 · see citing works
Aly, H., & Youssef, M. A. (2016) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2016.7562035 · Cited by 23 · see citing works
Stethoscope-Sensed Speech and Breath-Sounds for Person Identification With Sparse Training Data
Tran, V.-T., & Tsai, W.-H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2945364 · Cited by 23 · see citing works
BreathPro: Monitoring Breathing Mode during Running with Earables
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Every pixel counts: Fine-grained UI rendering analysis for mobile applications
Gao, Y. C., Luo, Y., Chen, D., Huang, H., Dong, W., Xia, M., Liu, X., & Bu, J. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057023 · Cited by 23 · see citing works
CondioSense: high-quality context-aware service for audio sensing system via active sonar
Li, F., Chen, H., Song, X., Zhang, Q., Li, Y., & Wang, Y. (2016) · Personal and Ubiquitous Computing · doi:10.1007/s00779-016-0981-1 · Cited by 22 · see citing works
Dual-Stream Contrastive Learning for Channel State Information Based Human Activity Recognition
Xu, K. H., Wang, J., Zhang, L., Zhu, H., & Zheng, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3219640 · Cited by 21 · see citing works
Non-Invasive Sleeping Posture Recognition and Body Movement Detection Based on RFID
Hu, X., Naya, K., Li, P., Miyazaki, T., Wang, K., & Sun, Y. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00302 · Cited by 20 · see citing works
Raheel, M. S., Tubbal, F., Raad, R., Ogunbona, P., Coyte, J., Patterson, C., Perlman, D., Iranmanesh, S., Odeh, N., & Foroughi, J. (2024) · Sensors & Diagnostics · doi:10.1039/d4sd00073k · Cited by 20 · see citing works
Ubiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications
Huang, J., Wang, Y.-S., Zou, Y., Wu, K., & Ni, L. M.-S. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-3073-5 · Cited by 18 · see citing works
Flappy Breath: A Smartphone-Based Breath Exergame
Stafford, M., Lin, F., & Xu, W. (2016) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2016.70 · Cited by 18 · see citing works
An Active Sleep Monitoring Framework Using Wearables
Hossain, H. M. S., Ramamurthy, S. R., Khan, M. A. A. H., & Roy, N. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185516 · Cited by 19 · see citing works
Leveraging Acoustic Signals for Fine-Grained Breathing Monitoring in Driving Environments
Xu, X., Yu, J., & Chen, Y. J. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3015828 · Cited by 17 · see citing works
Self-citationHan, F., Yang, P., Feng, Y., Jiang, W., Zhang, Y., & Li, X. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3659595 · Cited by 16 · see citing works
Cai, C., Zheng, R., & Hu, M. (2019) · Cited by 16 · see citing works
FinerSense: A Fine-Grained Respiration Sensing System Based on Precise Separation of Wi-Fi Signals
Song, W., Wang, Z., Guo, Y., Sun, Z., Ren, Z., Chen, C., Guo, B., Yu, Z., Zhou, X., & Zhang, D. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3514311 · Cited by 15 · see citing works
Acoustic-based vital signs monitoring
Wang, X., & Mao, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822281-2.00022-6 · Cited by 15 · see citing works
BARTON: Low Power Tongue Movement Sensing with In-Ear Barometers
Maag, B., Zhou, Z., Saukh, O., & Thiele, L. (2017) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2017.00013 · Cited by 15 · see citing works
BallistoBud: Heart Rate Variability Monitoring using Earbud Accelerometry for Stress Assessment
Islam, M. S., Rahman, M. M., Morshed, M. B., Lin, D. J., Li, Y.-Z., Zhou, H., Mendes, W. B., & Kuang, J.-L. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714029 · Cited by 16 · see citing works
Xu, Y., Li, Q., Tang, Z., Liu, J., & Xiang, B. (2021) · Sensors · doi:10.3390/s21206698 · Cited by 11 · see citing works
Respiratory Rate Estimation of Standing and Sitting People using WiFi Signals
Xu, Z., Guo, A., & Chen, L. (2020) · IEEE MTT-S International Wireless Symposium (IWS) · doi:10.1109/iws49314.2020.9360137 · Cited by 10 · see citing works
Towards Motion-Aware Passive Resting Respiratory Rate Monitoring Using Earbuds
Rahman, M. M., Ahmed, T., Ahmed, M. Y., Nemati, E., Dinh, M., Folkman, N., Hasan, M. M., Kuang, J., & Gao, J. A. (2021) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn51625.2021.9507016 · Cited by 10 · see citing works
Zhang, X., Wang, Y., Han, Y., Liang, C., Chatterjee, I., Tang, J., Yi, X., Patel, S., & Shi, Y. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3659616 · Cited by 9 · see citing works
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Harvill, J. B., Wani, Y. R., Alam, M., Ahuja, N., Hasegawa‐Johnson, M., Chestek, D. W., & Beiser, D. G. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871897 · Cited by 8 · see citing works
Portable prescreening system for sleep apnea
Guul, M. K., Jennum, P., & Sørensen, H. B. D. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591830 · Cited by 8 · see citing works
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El-Khadiri, Y., Corona, G., Rose, C., & Charpillet, F. (2018) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2018.00049 · Cited by 8 · see citing works
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Phokela, K. K., & Naik, V. S. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027297 · Cited by 8 · see citing works
Design of a sleep apnoea detection system for a home environment
Gaiduk, M., Weber, L., Alarcón, Á. S., Seepold, R., Madrid, N. M., Orcioni, S., & Conti, M. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.09.095 · Cited by 8 · see citing works
Rahman, M. M., Morshed, M. B., Rashid, N., Chandrashekhara, S., & Kuang, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3545471 · Cited by 7 · see citing works
Tracking Human Motions in Photographing
Yin, Y., Xie, L., Fan, Y., & Lu, S. (2017) · ACM Transactions on Sensor Networks · doi:10.1145/3085578 · Cited by 7 · see citing works
Human physiology and contactless vital signs monitoring using camera and wireless signals
Wang, X., & Shao, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822281-2.00008-1 · Cited by 7 · see citing works
Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones
Fukuyama, K., Sugiyama, O., Chin, K., Satou, S., Matsumoto, S., & Muto, M. (2022) · Advanced Biomedical Engineering · doi:10.14326/abe.11.58 · Cited by 7 · see citing works
Stream data analysis of body sensors for sleep posture monitoring: An automatic labelling approach
Jeng, P.-Y., & Wang, L. (2017) · Wireless and Optical Communications Conference · doi:10.1109/wocc.2017.7928969 · Cited by 7 · see citing works
Screening Sleep Disordered Breathing with Noncontact Measurement in a Clinical Site
Matsuura, Y., Jeong, H., Yamada, K., Watabe, K., Yoshimoto, K., & Ohno, Y. (2017) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2017.p0327 · Cited by 7 · see citing works
Paris: Passive and continuous fetal heart monitoring system
Yao, Y., Ning, Z., Zhang, Q., & Zhu, T. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100087 · Cited by 6 · see citing works
COVID-Beat: a low-cost breath monitoring approach for people in quarantine during the pandemic
Atif, M., Muralidharan, S., Ko, H., & Yoo, B. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac037 · Cited by 6 · see citing works
Chaperone: Real-time Locking and Loss Prevention for Smartphones
Chen, J., Hengartner, U., Khan, H., & Mannan, M. A. (2020) · USENIX Security Symposium · Cited by 6 · see citing works
Ma, D., Ferlini, A., & Cecilia Mascolo, C. (2021) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.70490 · Cited by 6 · see citing works
A Wearable Pendant Sensor to Monitor Compliance with Range of Motion Lymphatic Health Exercise
Wazir, H. K., Bethi, S. R., Kumar, A. R., Caruso, F., & Kapila, V. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175471 · Cited by 5 · see citing works
Wearables for In-Situ Monitoring of Cognitive States: Challenges and Opportunities
Radhakrishnan, M., Kandappu, T., Gulati, M., & Misra, A. (2023) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150270 · Cited by 5 · see citing works
Fukui, K., Ishimaru, S., Kato, T., & Numao, M. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00407-7 · Cited by 5 · see citing works
CHAR: Composite Head-Body Activities Recognition With a Single Earable Device
Zhu, P., Zhu, Y., Li, W., He, Y., Zou, Y., Wu, K., & Leung, V. C. M. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3548647 · Cited by 4 · see citing works
Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation
Sanz, P. R., Muñoz-Montoro, A. J., Torre-Cruz, J., Canadas-Quesada, F. J., & Ranilla, J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06411-3 · Cited by 4 · see citing works
CHAR: Composite Head-body Activities Recognition with A Single Earable Device
Zhu, P., Zou, Y., Li, W., & Wu, K. (2023) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom56429.2023.10099218 · Cited by 4 · see citing works
A SmartBed for Non-obtrusive Physiological Monitoring During Sleep: The LAID Project
Laurino, M., Carbonaro, N., Menicucci, D., Alfì, G., Gemignani, A., & Tognetti, A. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-49289-2_12 · Cited by 4 · see citing works
Bassetti, E., Cavallaccio, G., Marsico, M. D., & Panizzi, E. (2023) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3605390.3610832 · Cited by 3 · see citing works
Reprint of: Vibration sensing-based human and infrastructure safety/health monitoring: A survey
Valero, M., Li, F., Zhao, L., Zhang, C., Garrido, J. M., & Han, Z. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103572 · Cited by 3 · see citing works
Optimal respiratory waveform selection based on range-multiple beams using a MIMO radar
Wu, W., & Yang, Z. (2022) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1117/12.2631414 · Cited by 3 · see citing works
Ambient Sleep Quality Analysis with a Machine Learning Model
Bagci, M. F., Nguyen, T. Q., & Öztürk, Y. (2023) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw59220.2023.10193407 · Cited by 3 · see citing works
CORA: Continuous Respiration Monitoring Using Analytical Signal Processing
Zhou, J., Pu, H., Cao, H., Cai, C., Guo, P., & Jiang, H. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3437675 · Cited by 3 · see citing works
Breath Sound Acquisition in Different Environments
Ćirić, D., Bogunović, Đ., Janković, M., & Miletić, M. (2024) · International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) · doi:10.1109/icetran62308.2024.10735425 · Cited by 3 · see citing works
Wireless Wearable Respirator for Accurate Measurement of Breathing Parameters
Xiang, B., Xu, Y., Zhou, M., Zheng, Y., Zhan, L., Fan, J., Zhou, D., & Liu, J. (2019) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict.2019.8846401 · Cited by 3 · see citing works
Contact-free Breathing Rate Monitoring with Smartphones: A Sonar Phase Approach
Huang, R. (2018) · Cited by 3 · see citing works
Mitra, V., Chatterjee, A., Zhai, K., Weng, H. Y., Hill, A., Hay, N., Webb, C., Cheng, J., & Azemi, E. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101926 · Cited by 2 · see citing works
WiFi Amplitude and Phase-Based Respiratory Rate Monitoring
Ge, Y., & Ho, I. W. (2024) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2024-spring62846.2024.10683375 · Cited by 2 · see citing works
Behavicker: Eavesdropping Computer-Usage Activities Through Acoustic Side Channel
Chen, M., Lin, J., Liu, W., & Wu, K. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/8090652 · Cited by 2 · see citing works
- Authors
- Ren, Y., Wang, C., Yang, J., & Chen, Y.
- Affiliations
- Stevens Institute of Technology (US)
- Florida State University (US)
- Venue
- 2015 IEEE Conference on Computer Communications (INFOCOM)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 1194-1202
- OpenAlex
- W1535687102
- Cited by
- 119
- References
- 16
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 14 · 2020: 13 · 2019: 14 · 2018: 13 · 2017: 14 · 2016: 6
- FWCI
- 21.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Computer Communications (INFOCOM) (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Keywords
- sleep monitoring, breathing rate detection, smartphone sensors, acoustic signal processing, noise reduction, snoring, cough, sleep apnea monitoring, non-invasive vital sign monitoring
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