A qualitative study on commercially available: Sleep monitoring systems and sleep analysis tools

2021 ◽  
Vol 14 (1) ◽  
pp. 413-419
Author(s):  
Hasina Adil ◽  
A.A. Koser ◽  
Alok Gupta
Keyword(s):  
Qualitative Study ◽  
Monitoring Systems ◽  
Sleep Monitoring ◽  
Analysis Tools ◽  
Sleep Analysis

scholarly journals Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

10.2196/20921 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e20921
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.

scholarly journals Sleep quality assessment by parameter optimization

2021 ◽  
Vol 2070 (1) ◽  
pp. 012013
Author(s):  
H Adil ◽  
A A Koser ◽  
M S Qureshi ◽  
A Gupta
Keyword(s):  
Sleep Quality ◽  
Parameter Optimization ◽  
Quality Measurement ◽  
Dynamic Parameter ◽  
Close Correlation ◽  
Optimization Method ◽  
Accurate Information ◽  
Sleep Monitoring ◽  
Sleep Analysis ◽  
Process Complexity

Abstract Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is based on parameter reduction by finding key parameters and their inter dependent parameters. Sleep monitoring by these optimize parameter is different from both, clinical complex (PSG) used in hospitals and commercially available devices which work on dependent and dynamic parameter sensing. Optimized parameters obtained from PSG parameters are Electrocardiogram (ECG), Electrooculogram (EOG), Electroencephalography (EEG) and Cerebral blood flow (CBF). These key parameters show close correlation with sleep and hence reduce complexity in sleep monitoring by providing simultaneous measurement of appropriate signals for sleep analysis.

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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 168486-168501
Author(s):  
Deland Hu Liu ◽  
Syed Anas Imtiaz
Keyword(s):  
Monitoring Systems ◽  
Sleep Monitoring

scholarly journals Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study

10.2196/24339 ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. e24339
Author(s):  
Maartje M S Hendriks ◽  
Jaap H van Lotringen ◽  
Marije Vos-van der Hulst ◽  
Noël L W Keijsers
Keyword(s):  
Inpatient Rehabilitation ◽  
Sensor Data ◽  
Sensor Technology ◽  
Movement Activity ◽  
Stroke Patients ◽  
Sleep Monitoring ◽  
Sleep Analysis ◽  
Cord Injury ◽  
Sleep Assessment ◽  
Hospital Rehabilitation

Background Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.

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

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.

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

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.

scholarly journals Portable Sleep Monitoring Systems: Broadening the Horizons

2017 ◽  
Vol 13 (06) ◽  
pp. 773-774
Author(s):  
Naima Covassin ◽  
Virend K. Somers
Keyword(s):  
Monitoring Systems ◽  
Sleep Monitoring ◽  
Portable Sleep Monitoring

scholarly journals Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study (Preprint)

2020 ◽  
Author(s):  
Maartje M S Hendriks ◽  
Jaap H van Lotringen ◽  
Marije Vos-van der Hulst ◽  
Noël L W Keijsers
Keyword(s):  
Inpatient Rehabilitation ◽  
Sensor Data ◽  
Sensor Technology ◽  
Movement Activity ◽  
Stroke Patients ◽  
Sleep Monitoring ◽  
Sleep Analysis ◽  
Cord Injury ◽  
Sleep Assessment ◽  
Hospital Rehabilitation

BACKGROUND Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. OBJECTIVE The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. METHODS Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. RESULTS In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (<i>t</i><sub>18</sub>=−2.1, <i>P</i>=.04) and movement activity (<i>t</i><sub>18</sub>=−1.2, <i>P</i>=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. CONCLUSIONS It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.

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