Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review

被引:25
作者
Hussain, Zawar [1 ]
Sheng, Quan z. [1 ]
Zhang, Wei emma [2 ]
Ortiz, Jorge [3 ]
Pouriyeh, Seyedamin [4 ]
机构
[1] Macquarie Univ, Sydney, NSW 2109, Australia
[2] Univ Adelaide, Adelaide, SA 2109, Australia
[3] Rutgers State Univ, New Brunswick, NJ USA
[4] Kennesaw State Univ, Kennesaw, GA USA
来源
ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE | 2022年 / 3卷 / 02期
基金
澳大利亚研究理事会;
关键词
APNEA; HEALTH; STAGE; POPULATION; DISORDERS; RESOURCE; QUALITY; SYSTEM; SENSOR; ADULT;
D O I
10.1145/3491245
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.
引用
收藏
页数:26
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