Paper Info
Title
Computational Sleep Behavior Analysis: A Survey.
Abstract
Sleep is a key marker of health, as it can either be a cause or a consequence. It is traditionally studied in clinical environments using dedicated medical devices. Recent technological developments, e.g., in sensing and data analysis, have led to new approaches for sleep monitoring and assessment, which are attracting increasing attention in the emerging domain of personalized smart healthcare. Nevertheless, a high-level overview of technology-enabled research on sleep that can inform related communities of the latest developments is lacking. In this paper, we present a comprehensive review to examine the current status of various aspects of technology-based sleep research. We first characterize sleep behavior and key areas of sleep assessment, and we introduce a general review of the methodologies used in this domain. We review the major technological methods and trends associated with sleep monitoring, data collection and sleep behavior analysis, from which strengths and weaknesses are highlighted. Finally, we also discuss challenges and promising directions for future research.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2944801
IEEE ACCESS
Keywords
DocType
Volume
Sleep apnea,Monitoring,Diseases,Biomedical monitoring,Data mining,Medical diagnostic imaging,Sleep behavior analysis,home environment,wearables,polysomnography,actigraphy,sleep stage classification,sleep positions,sleep disorders,disease recognition,data mining,machine learning,deep learning,sleep monitoring,sleep parameters
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Sarah Fallmann101.35
Liming Chen22607201.71