Studying the Effects of Compression in EEG-Based Wearable Sleep Monitoring Systems

Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

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Considerations for integration of a physiological radar monitoring system with gold standard clinical sleep monitoring systems

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2013.6609952 ◽

2013 ◽

Cited By ~ 6

Author(s):

Aditya Singh ◽

Mehran Baboli ◽

Xiaomeng Gao ◽

Ehsan Yavari ◽

Bryson Padasdao ◽

...

Keyword(s):

Monitoring System ◽

Gold Standard ◽

Monitoring Systems ◽

Sleep Monitoring ◽

Radar Monitoring

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Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review (Preprint)

10.2196/preprints.20921 ◽

2020 ◽

Author(s):

Qiang Pan ◽

Damien Brulin ◽

Eric Campo

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Mining ◽

Big Data ◽

Monitoring System ◽

The Body ◽

Monitoring Systems ◽

Sleep Monitoring ◽

Advantages And Disadvantages ◽

System Solution

BACKGROUND Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. OBJECTIVE This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. METHODS This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. RESULTS By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. CONCLUSIONS Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

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Physical Thorax Model and 2D Grid of Force Sensors to Monitor Respiration

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2019-11238 ◽

2019 ◽

Author(s):

Matthew R. Dean ◽

Noah J. Martins ◽

Joseph D. Brown ◽

James McCusker ◽

Guohua Ma ◽

...

Keyword(s):

Physical Model ◽

Human Subjects ◽

Monitoring Systems ◽

Force Sensors ◽

Prototype System ◽

Volume Changes ◽

Sleep Monitoring ◽

Early Testing ◽

The Cost

Abstract Sleep disorders impair the quality of life for many individuals, but often go undiagnosed and untreated due to the cost and sleep-disturbing aggravation of polysomnography, the clinical sleep test. Simpler sleep monitoring systems that could be used at home may provide useful health information. A 2D grid of force sensors within a mat beneath the thorax of a sleeping subject has been reported to enable monitoring of respiration during sleep. A physical model of a thorax over such a 2D grid of force sensors may enable more tests and perturbations of parameters than could be done using only human subjects. The purpose of this project was to develop and test a physical model of a thorax undergoing volume changes, and measuring the changes in force by a grid of force sensors under the model. A prototype system was developed. Early testing shows promise for being able to monitor the changes in force as volume of the model changes. More development and testing are required toward development of improved algorithms and systems for sleep monitoring mats.

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