The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar
Abstract
In this paper, we present DoppleSleep -- a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user's body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smartphone application that are benchmarked in terms of power and resource usage.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In the past decade, high frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
In the citing paper
“Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion from these findings71,73–75.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
In the citing paper
“The method has been shown to detect physical movements with an 86% recall rate and error rates of <10%71.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [71]
Citations
Cites
(2007) · Cited by 6,530
(1998) · Cited by 3,476
The role and validity of actigraphy in sleep medicine: an update
Sadeh, A. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2010.10.001 · Cited by 1,415
The Role of Actigraphy in the Evaluation of Sleep Disorders
Sadeh, A., Hauri, P., Kripke, D. F., & Lavie, P. (1995) · SLEEP · doi:10.1093/sleep/18.4.288 · Cited by 914
A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring
Li, C., Lubecke, V. M., Borić-Lubecke, O., & Lin, J. (2013) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2013.2256924 · Cited by 826
Droitcour, A. D., Borić-Lubecke, O., Lubecke, V. M., Lin, J., & Kovacs, G. T. A. (2004) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2004.823552 · Cited by 662
Arctangent Demodulation With DC Offset Compensation in Quadrature Doppler Radar Receiver Systems
Park, B.-K., Borić-Lubecke, O., & Lubecke, V. M. (2007) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2007.895653 · Cited by 605
Further Validation of Actigraphy for Sleep Studies
de Souza, L., Benedito‐Silva, A. A., Pires, M. L. N., Poyares, D. R., Tufik, S., & Calil, H. M. (2003) · SLEEP · doi:10.1093/sleep/26.1.81 · Cited by 587
Chen, K., Huang, Y. G., Zhang, J., & Norman, A. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.817625 · Cited by 445
Noninvasive microwave measurement of respiration
Lin, J. C. (1975) · Proceedings of the IEEE · doi:10.1109/proc.1975.9992 · Cited by 432
Research works printed but not linked: 2
- C. Iber et al. The american academy of sleep medicine manual for the scoring of sleep and associated events . American Academy of Sleep Medicine ( 2007 ). C. Iber et al. The american academy of sleep medicine manual for the scoring of sleep and associated events. American Academy of Sleep Medicine (2007).
- Xiao , Y. , Lin , J. , Boric-Lubecke , O. , and Lubecke , V. M . Frequency-tuning technique for remote detection of heartbeat and respiration using low-power double-sideband transmission in the ka-band. Microwave Theory and Techniques , IEEE Transactions on 54 , 5 ( 2006 ), 2023--2032. Xiao, Y., Lin, J., Boric-Lubecke, O., and Lubecke, V. M. Frequency-tuning technique for remote detection of heartbeat and respiration using low-power double-sideband transmission in the ka-band. Microwave Theory and Techniques, IEEE Transactions on 54, 5 (2006), 2023--2032.
Cited by
Sorted by citations
Soli: ubiquitous gesture sensing with millimeter wave radar
Lien, J., Gillian, N., Karagozler, M. E., Amihood, P., Schwesig, C., Olson, E., Raja, H., & Poupyrev, I. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925953 · Cited by 957
A Survey of Wearable Devices and Challenges
Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S., Thilakarathna, K., Hassan, M., & Seneviratne, A. P. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2731979 · Cited by 880
Li, X., Li, S., Zhang, D., Xiong, J., Wang, Y., & Mei, H. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971665 · Cited by 330
The future of sleep health: a data-driven revolution in sleep science and medicine
Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., Garcia-Gomez, J. M., Taheri, S., Guan, Y., & Fernandez-Luque, L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0244-4 · Cited by 311
Extracting Multi-Person Respiration from Entangled RF Signals
Yue, S., He, H., Wang, H., Rahul, H. S., & Katabi, D. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3214289 · Cited by 198
Using Smart City Technology to Make Healthcare Smarter
Cook, D. J., Duncan, G. E., Sprint, G., & Fritz, R. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2787688 · Cited by 184
Palipana, S., Salami, D., Leiva, L. A., & Sigg, S. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448110 · Cited by 179
Yao, S., Zhao, Y., Shao, H., Liu, S., Liu, D., Su, L., & Abdelzaher, T. (2018) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3274783.3274840 · Cited by 149
SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System
Lin, F., Zhuang, Y., Song, C. L., Wang, A., Li, Y., Gu, C., Li, C., & Xu, W. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2541680 · Cited by 138
WiFi-Sleep: Sleep Stage Monitoring Using Commodity Wi-Fi Devices
Yu, B., Wang, Y., Niu, K., Zeng, Y., Gu, T., Wang, L., Guan, C., & Zhang, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3068798 · Cited by 139
SMARS: Sleep Monitoring via Ambient Radio Signals
Zhang, F., Wu, C., Wang, B., Wu, M. H., Bugos, D., Zhang, H., & Liu, K. J. R. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2939791 · Cited by 137
Diraco, G., Leone, A., & Siciliano, P. A. (2017) · Biosensors · doi:10.3390/bios7040055 · Cited by 135
Zhang, Y., Yang, C. J., Hudson, S. E., Harrison, C., & Sample, A. P. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173847 · Cited by 132
Soon, S., Svavarsdottir, H. S., Downey, C. L., & Jayne, D. G. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000354 · Cited by 137
Xu, 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
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
Learning Longterm Representations for Person Re-Identification Using Radio Signals
Fan, L., Li, T., Fang, R., Hristov, R., Yuan, Y., & Katabi, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01071 · Cited by 122
BodyCompass: Monitoring Sleep Posture with Wireless Signals
Yue, S., Yang, Y., Wang, H., Rahul, H., & Katabi, D. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3397311 · Cited by 112
Yamada, Y., & Kobayashi, M. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.06.005 · Cited by 122
Vilardaga, R., Rizo, J., Zeng, E., Kientz, J. A., Ries, R., Otis, C., & Hernandez, K. (2018) · JMIR Serious Games · doi:10.2196/games.8881 · Cited by 90
Self-citationA Review of Approaches for Sleep Quality Analysis
Mendonça, F., Mostafa, S. S., Morgado‐Dias, F., Ravelo-García, A. G., & Penzel, T. (2019) · IEEE Access · doi:10.1109/access.2019.2900345 · Cited by 117
Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals
Hsu, C.-Y., Ahuja, A., Yue, S., Hristov, R., Kabelac, Z., & Katabi, D. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130924 · Cited by 97
Ravichandran, R., Sien, S.-W., Patel, S., Kientz, J. A., & Pina, L. R. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025557 · Cited by 95
Self-citationEnabling Identification and Behavioral Sensing in Homes using Radio Reflections
Hsu, C.-Y., Hristov, R., Lee, G.-H., Zhao, M., & Katabi, D. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300778 · Cited by 95
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Piriyajitakonkij, M., Warin, P., Lakhan, P., Leelaarporn, P., Kumchaiseemak, N., Suwajanakorn, S., Pianpanit, T., Niparnan, N., Mukhopadhyay, S. C., & Wilaiprasitporn, T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3025900 · Cited by 87
RF Vital Sign Sensing under Free Body Movement
Gong, J., Zhang, X., Lin, K., Ren, J., Zhang, Y., & Qiu, W. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478090 · Cited by 87
Automated sensing of daily activity: A new lens into development
de Barbaro, K. (2019) · Developmental Psychobiology · doi:10.1002/dev.21831 · Cited by 84
Technologies for sleep monitoring at home: wearables and nearables
Yoon, H., & Choi, S. H. (2023) · Biomedical Engineering Letters · doi:10.1007/s13534-023-00305-8 · Cited by 82
Microwave Sensing and Sleep: Noncontact Sleep-Monitoring Technology With Microwave Biomedical Radar
Hong, H., Zhang, L., Zhao, H., Chu, H., Gu, C., Brown, M., Zhu, X., & Li, C. (2019) · IEEE Microwave Magazine · doi:10.1109/mmm.2019.2915469 · Cited by 76
Blood Glucose Level Monitoring Using an FMCW Millimeter-Wave Radar Sensor
Omer, A. E., Safavi‐Naeini, S., Hughson, R. L., & Shaker, G. S. A. (2020) · Remote Sensing · doi:10.3390/rs12030385 · Cited by 75
Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks
Sano, A., Chen, W. '., Lopez-Martinez, D., Taylor, S., & Picard, R. W. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2867619 · Cited by 73
SilentTalk: Lip reading through ultrasonic sensing on mobile phones
Tan, J., Nguyen, C.-T., & Wang, X. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057099 · Cited by 66
Design Guideline for Developing Safe Systems that Apply Electricity to the Human Body
Kono, M., Takahashi, T., Nakamura, H., Miyaki, T., & Rekimoto, J. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3184743 · Cited by 63
Computational Sleep Behavior Analysis: A Survey
Fallmann, S., & Chen, L. (2019) · IEEE Access · doi:10.1109/access.2019.2944801 · Cited by 58
Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation
Valle, V. I.-D., Silva, J., Navarro-Illana, E., & Cauli, O. (2019) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1693890 · Cited by 56
WiFind: Driver Fatigue Detection with Fine-Grained Wi-Fi Signal Features
Jia, W., Peng, H., Ruan, N., Tang, Z., & Zhao, W. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2848969 · Cited by 56
Multi-Breath: Separate Respiration Monitoring for Multiple Persons with UWB Radar
Yang, Y., Cao, J., Liu, X., & Liu, X. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00124 · Cited by 55
Zhu, K., Li, M. H., Akbarian, S., Hafezi, M., Yadollahi, A., & Taati, B. (2019) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2019.2946147 · Cited by 50
Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar
Kiriazi, J. E., Islam, S. M. M., Borić-Lubecke, O., & Lubecke, V. M. (2021) · IEEE Access · doi:10.1109/access.2021.3062385 · Cited by 48
Improved Vehicle Steering Pattern Recognition by Using Selected Sensor Data
Ouyang, Z., Niu, J., & Guizani, M. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2762679 · Cited by 46
Liu, C., Xiong, J., Cai, L., Feng, L., Chen, X., & Fang, D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351254 · Cited by 47
A fast and scalable approach for IoT service selection based on a physical service model
Jin, X., Chun, S., Jung, J., & Lee, K.-H. (2016) · Information Systems Frontiers · doi:10.1007/s10796-016-9650-1 · Cited by 43
On Goodness of WiFi Based Monitoring of Sleep Vital Signs in the Wild
Ali, K., Alloulah, M., Kawsar, F., & Liu, A. X. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3077533 · Cited by 41
Kiaghadi, A., Homayounfar, S. Z., Gummeson, J., Andrew, T. L., & Ganesan, D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351247 · Cited by 40
An IoT-based smart pillow for sleep quality monitoring in AAL environments
Veiga, A., García, L., Parra, L., Lloret, J., & Augele, V. (2018) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2018.8364061 · Cited by 39
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
Exploring Tangible Interactions with Radar Sensing
Yeo, H.-S., Minami, R., Rodriguez, K., Shaker, G. S. A., & Quigley, A. J. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287078 · Cited by 36
Yang, Y., Cao, J., & Liu, X. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369808 · Cited by 37
Islam, S. M. M., & Lubecke, V. M. (2022) · IEEE Sensors Letters · doi:10.1109/lsens.2022.3148378 · Cited by 37
Hong, N. T. T., Giang, N. L., Huy, N. Q., Manh, D. V., Tran, D.-N., & Tran, D.-T. (2023) · Measurement · doi:10.1016/j.measurement.2023.113231 · Cited by 35
Implementation of Unobtrusive Sensing Systems for Older Adult Care: Scoping Review
Sharma, N., Brinke, J. K., van Gemert‐Pijnen, J. E. W. C., & Braakman‐Jansen, A. (2021) · JMIR Aging · doi:10.2196/27862 · Cited by 39
Noncontact Sleep Study by Multi-Modal Sensor Fusion
Chung, K.-Y., Song, K., Shin, K., Sohn, J., Cho, S. H., & Chang, J. (2017) · Sensors · doi:10.3390/s17071685 · Cited by 33
Mohamed, R., & Youssef, M. A. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3132028 · Cited by 33
SleepPoseNet:Multi-view learning for sleep postural transition recognition using UWB
Piriyajitakonkij, M. (2020) · Research Explorer (The University of Manchester) · Cited by 32
Improving Stress Management and Sleep Hygiene in Intelligent Homes
Leonidis, A., Korozi, M., Sykianaki, E., Tsolakou, E., Kouroumalis, V., Ioannidi, D., Stavridakis, A., Antona, M., & Stephanidis, C. (2021) · Sensors · doi:10.3390/s21072398 · Cited by 36
Bioradiolocation-based sleep stage classification
Tataraidze, A., Korostovtseva, L., Anishchenko, L., Bochkarev, M., Sviryaev, Y., & Ivashov, S. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591321 · Cited by 30
Bari, R., Adams, R. J., Rahman, M. M., Parsons, M. B., Buder, E. H., & Kumar, S. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191734 · Cited by 28
RFGAN: RF-Based Human Synthesis
Yu, C., Wu, Z., Zhang, D., Lu, Z. P., Hu, Y., & Chen, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3153136 · Cited by 28
Hussain, Z., Sagar, S., Zhang, W. E., & Sheng, Q. Z. (2019) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3360774.3360797 · Cited by 26
Kalyanaraman, A., Hong, D., Soltanaghaei, E., & Whitehouse, K. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130926 · Cited by 27
ApneaRadar: A 24GHz Radar-Based Contactless Sleep Apnea Detection System
Du, N., Liu, K., Ge, L., & Zhang, J. (2017) · International Conference on Frontiers of Sensors Technologies (ICFST) · doi:10.1109/icfst.2017.8210539 · Cited by 27
Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
Nagy, Á., Földesy, P., Jánoki, I. G., Terbe, D., Siket, M., Szabó, M., Varga, J., & Zarándy, Á. (2021) · Applied Sciences · doi:10.3390/app11167215 · Cited by 26
Laput, G., & Harrison, C. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300559 · Cited by 26
Wang, P., Ma, X., Zheng, R., Chen, L., Zhang, X., Zeghlache, D., & Zhang, D. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3340925 · Cited by 26
A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring
Krauss, D., Engel, L., Ott, T., Bräunig, J., Richer, R., Gambietz, M., Albrecht, N. C., Hille, E. M., Ullmann, I., Braun, M., et al. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3397208 · Cited by 32
Pucihar, K. Č., Attygalle, N. T., Kljun, M., Sandor, C., & Leiva, L. A. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3532212 · Cited by 24
RadarHand: A Wrist-Worn Radar for On-Skin Touch-Based Proprioceptive Gestures
Hajika, R., Gunasekaran, T. S., Haigh, C. D. S. Y., Pai, Y. S., Hayashi, E., Lien, J., Lottridge, D. M., & Billinghurst, M. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3617365 · Cited by 24
Robust RFID-based Respiration Monitoring in Dynamic Environments
Yang, Y., Cao, J., & Wang, Y. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3106954 · Cited by 21
SoK: Context Sensing for Access Control in the Adversarial Home IoT
He, W., Zhao, V., Morkved, O., Siddiqui, S., Fernandes, E., Hester, J. D., & Ur, B. (2021) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp51992.2021.00014 · Cited by 21
From Self-Tracking to Sleep-Hacking
Karlgren, K., Brown, B. A. T., & Mcmillan, D. (2022) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3555630 · Cited by 22
Feasibility Assessment of Wearable Respiratory Monitors for Ambulatory Inhalation Topography
Jayasekera, S., Hensel, E. C., & Robinson, R. J. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18062990 · Cited by 20
Rostaminia, S., Homayounfar, S. Z., Kiaghadi, A., Andrew, T. L., & Ganesan, D. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3513023 · Cited by 20
No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing
Attygalle, N. T., Leiva, L. A., Kljun, M., Sandor, C., Plopski, A., Kato, H., & Pucihar, K. Č. (2021) · Sensors · doi:10.3390/s21175771 · Cited by 20
A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection
Li, X., Liu, B., Liu, Y., Li, J., Lai, J., & Zheng, Z. (2019) · Sensors · doi:10.3390/s19214751 · Cited by 19
Engagement-Free and Contactless Bed Occupancy and Vital Signs Monitoring
Song, Y., Li, B., Luo, D., Xie, Z., Phillips, B. G., Yuan, K., & Song, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3316674 · Cited by 18
RF-Egg: An RF Solution for Fine-Grained Multi-Target and Multi-Task Egg Incubation Sensing
Sun, Z., Ni, T., Chen, Y., Duan, D., Liu, K., & Xu, W. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3649378 · Cited by 18
Unobtrusive acquisition of cardiorespiratory signals
Zaunseder, S., Henning, A., Wedekind, D., Trumpp, A., & Malberg, H. (2017) · Somnologie - Schlafforschung und Schlafmedizin · doi:10.1007/s11818-017-0112-x · Cited by 18
SleepGuru: Personalized Sleep Planning System for Real-life Actionability and Negotiability
Lee, J., Kim, S.-N., Cheon, M., Ju, H., Lee, J., & Hwang, I. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545709 · Cited by 18
The role of telemedicine in obstructive sleep apnea management
Lugo, V. M., Villanueva, J. A., Garmendia, O., & Montserrat, J. M. (2017) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2017.1343147 · Cited by 17
A-spiro: Towards continuous respiration monitoring
Whitlock, J., Sill, J. M., & Jain, S. (2020) · Smart Health · doi:10.1016/j.smhl.2019.100105 · Cited by 17
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
Cardiac and Respiratory Sensing from a Hovering UAV Radar Platform
Rong, Y., Herschfelt, A., Holtom, J., & Bliss, D. W. (2021) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp49050.2021.9513771 · Cited by 17
K-band Doppler radar for contact-less overnight sleep marker assessment: a pilot validation study
Vasireddy, R., Roth, C., Mathis, J., Goette, J., Jacomet, M., & Vogt, A. P. (2017) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-017-0060-9 · Cited by 17
Material Identification Using RF Sensors and Convolutional Neural Networks
Agresti, G., & Milani, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682296 · Cited by 17
Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement
Hwang, S. H., Lee, Y. J., Jeong, D. U., & Park, K. S. (2016) · Methods of Information in Medicine · doi:10.3414/me15-01-0140 · Cited by 16
A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments
Li, Y.-X., Huang, J.-L., Yao, X., Mu, S., Zong, S., & Shen, Y. (2024) · Scientific Data · doi:10.1038/s41597-024-03950-5 · Cited by 16
Nocera, A., Senigagliesi, L., Raimondi, M., Ciattaglia, G., & Gambi, E. (2024) · IEEE Access · doi:10.1109/access.2024.3482690 · Cited by 21
Yang, Q., Wu, H., Huang, Q., Zhang, J., Chen, H., Li, W., Tao, X., & Zhang, Q. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569498 · Cited by 16
New Paths in Respiratory Sleep Medicine
Penzel, T., Dietz-Terjung, S., Woehrle, H., & Schöbel, C. (2021) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2021.08.006 · Cited by 13
Perspective—Longitudinal Sleep Monitoring for All: Payoffs, Challenges and Outlook
Andrew, T. L., Rostaminia, S., Homayounfar, S. Z., & Ganesan, D. (2022) · ECS Sensors Plus · doi:10.1149/2754-2726/ac59c1 · Cited by 13
Zakaria, C., Yılmaz, G., Mammen, P. M., Chee, M. W. L., Shenoy, P. J., & Balan, R. K. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569489 · Cited by 12
Zhang, R., Wang, Z., Li, G., Wang, Y., Shuai, J., Zheng, J., Huang, J., & Bao, S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3215443 · Cited by 12
Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
Yoo, Y., Jung, C., & Shin, H. (2023) · Applied Sciences · doi:10.3390/app13074468 · Cited by 12
Hypnos: A Contactless Sleep Stage Monitoring System Using UWB Signals
Li, S., Jin, B., Zhang, F., Wang, Z., Ma, J., Ren, X., & Liu, H. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3678581 · Cited by 12
Robust RFID-based Respiration Monitoring in Dynamic Environments
Yang, Y., & Cao, J. (2020) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon48991.2020.9158419 · Cited by 12
Designing for Extreme Sleepers: Rethinking the Rhythms of Sleep Technology
Karlgren, K., & McMillan, D. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3546155.3546685 · Cited by 11
Han, J., Men, A., Liu, Y., Yao, Z., Zhang, S., Yan, Y., & Chen, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3300891 · Cited by 11
A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar
Li, Z., Chen, K., & Xie, Y. (2024) · Sensors · doi:10.3390/s24185900 · Cited by 11
Improved Heartbeat Detection by Exploiting Temporal Phase Coherency in FMCW Radar
Choi, H.-I., Song, W., Song, H., & Shin, H. (2021) · IEEE Access · doi:10.1109/access.2021.3132608 · Cited by 11
Oshim, M. F. T., Killingback, J., Follette, D., Peng, H., & Rahman, T. (2020) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3379337.3415902 · Cited by 11
Self-citation
Metadata
- Authors
- Rahman, T., Adams, A. T., Ravichandran, R. V., Zhang, M., Patel, S. N., Kientz, J. A., & Choudhury, T.
- Affiliations
- Cornell University (US)
- University of Washington (US)
- Michigan State University (US)
- ORCID
- Venue
- Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2015-09-07
- Pages
- 39-50
- OpenAlex
- W2055483942
- Cited by
- 175
- References
- 25
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 17 · 2023: 13 · 2022: 23 · 2021: 21 · 2020: 19 · 2019: 23 · 2018: 19 · 2017: 17 · 2016: 11 · 2015: 1
- FWCI
- 30.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Funders
- ISTC (Intel Science & Technology Center on Pervasive Computing)
- National Science Foundation (IIS #1344587, IIS #1202141, CBET #1343058, IIS #1344613)
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- contactless sleep monitoring, Doppler radar, sleep quality, sleep stage classification, REM sleep detection, human motion detection, sleep latency, awakening, sleep efficiency, ARM microcontroller, mobile application, power consumption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
