research-article

Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals

Authors:

Zachary Kabelac,

Dina KatabiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 59, Pages 1 - 18

https://doi.org/10.1145/3130924

Published: 11 September 2017 Publication History

Abstract

Insomnia is the most prevalent sleep disorder in the US. In-home insomnia monitoring is important for both diagnosis and treatment. Existing solutions, however, require the user to either maintain a sleep diary or wear a sensor while sleeping. Both can be quite cumbersome. This paper introduces EZ-Sleep, a new approach for monitoring insomnia and sleep. EZ-Sleep has three properties. First, it is zero effort, i.e., it neither requires the user to wear a sensor nor to record any data. It monitors the user remotely by analyzing the radio signals that bounce off her body. Second, it delivers new features unavailable with other devices such as automatically detecting where the user sleeps and her exact bed schedule, while simultaneously monitoring multiple users in different beds. Third, it is highly accurate. Its average error in measuring sleep latency and total sleep time is 4.9 min and 10.3 min, respectively.

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 July 2017

Received: 01 May 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Erdélyi VMizumoto TKitai YIshimaru DAdachi HHigashino TIkeda M(2025)Detecting Subtle Signs of School Attendance Issues Using Smartphone-Based SensingIEEE Access10.1109/ACCESS.2024.352310813(4652-4669)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3523108
Xu JSmaling HSchoones JAchterberg Wvan der Steen J(2024)Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping reviewBMC Palliative Care10.1186/s12904-024-01371-023:1Online publication date: 21-Mar-2024
https://doi.org/10.1186/s12904-024-01371-0
Popp ZLow SIgwe ARahman MKim MKhan ROh EKumar ADe Anda‐Duran IDing HHwang PSunderaraman PShih LLin HKolachalama VAu R(2024)Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the ResearchJournal of the American Heart Association10.1161/JAHA.123.03124713:2Online publication date: 16-Jan-2024
https://doi.org/10.1161/JAHA.123.031247
Mary Mammen PZakaria CMolom-Ochir TTrivedi AShenoy PBalan R(2024)WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised LearningACM Journal on Computing and Sustainable Societies10.1145/37057223:1(1-25)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3705722
Pourpanah FEtemad A(2024)Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive SurveyACM Transactions on Computing for Healthcare10.1145/36708545:4(1-43)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3670854
Khalid MKlerman EMcHill APhillips ASano A(2024)SleepNetProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435088:1(1-34)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643508
He QYang EFang S(2024)A Survey on Human Profile Information Inference via Wireless SignalsIEEE Communications Surveys & Tutorials10.1109/COMST.2024.337339726:4(2577-2610)Online publication date: 1-Oct-2024
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Gu XLiu HZhou MZhang HWang S(2024)Design of mmWave Radar Antenna Array and its Application in Respiratory Health Monitoring2024 IEEE 12th Asia-Pacific Conference on Antennas and Propagation (APCAP)10.1109/APCAP62011.2024.10881804(1-2)Online publication date: 22-Sep-2024
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Zhai BElder GGodfrey A(2024)Challenges and opportunities of deep learning for wearable-based objective sleep assessmentnpj Digital Medicine10.1038/s41746-024-01086-97:1Online publication date: 4-Apr-2024
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Lee TLee HLee JKim J(2023)Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health PatientsSensors10.3390/s2320854423:20(8544)Online publication date: 18-Oct-2023
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