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An Active Sleep Monitoring Framework Using Wearables

Authors:

H. M. Sajjad Hossain,

Sreenivasan R. Ramamurthy,

Md Abdullah Al Hafiz Khan,

Nirmalya RoyAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 8, Issue 3

Article No.: 22, Pages 1 - 30

https://doi.org/10.1145/3185516

Published: 13 July 2018 Publication History

Abstract

Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person’s fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, recognition, and assessment at scale. For a larger deployment of our proposed model across a community of individuals, we propose an active-learning-based methodology to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.

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Cited By

Arora A(2024)IoT-Enabled Smart Mental Health Assessment Using Deep Hybrid Regression Models Over Actigraph-Based Sequential Motor Activity DataArabian Journal for Science and Engineering10.1007/s13369-024-08739-7Online publication date: 9-Feb-2024
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An Active Sleep Monitoring Framework Using Wearables

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cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 8, Issue 3

September 2018

235 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3236465

Editor:
Michelle X. Zhou
Juji, Inc., USA

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Copyright © 2018 ACM.

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Publication History

Published: 13 July 2018

Accepted: 01 February 2018

Revised: 01 January 2018

Received: 01 May 2017

Published in TIIS Volume 8, Issue 3

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https://doi.org/10.1007/s13369-024-08739-7
Arora AChakraborty PBhatia MKumar A(2023) Deep‐SQA : A deep learning model using motor activity data for objective sleep quality assessment assisting digital wellness in healthcare 5.0 Expert Systems10.1111/exsy.13321Online publication date: 8-May-2023
https://doi.org/10.1111/exsy.13321
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https://doi.org/10.3390/su14020990
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Arora AChakraborty PBhatia M(2022)Intelligent Behavioral Health Analysis in Youth Using Real-Time Smartphone and Wearable DataProceedings of International Conference on Communication and Computational Technologies10.1007/978-981-19-3951-8_49(653-666)Online publication date: 27-Sep-2022
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