A unique unobtrusive intelligent sleep monitoring (ISM) method based on parameter optimization for sleep analysis

2020 ◽  
Author(s):  
Hasina Adil ◽  
A. A. Koser ◽  
Alok Gupta
Keyword(s):  
Parameter Optimization ◽  
Sleep Monitoring ◽  
Sleep Analysis

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 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 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.

Оптимизация параметров машины для уборки лука-репки с укладкой в валок

2018 ◽  
Author(s):  
I.I. Irkov ◽  
V.G. Selivanov ◽  
N.V. Romanovskiy
Keyword(s):  
Soil Moisture ◽  
Operating Procedure ◽  
Parameter Optimization ◽  
Stable Operating

Изложены результаты оптимизации параметров машины для уборки лука-репки с укладкой в валок. Установлено, что покрытие фторопластом подкапывающих ножей, выполненных по криволинейной поверхности, и опорного катка обеспечивает стабильный технологический процесс машины при влажности почвы до 26%.The results of parameter optimization for onion harvesting machine with swath emplacement are stated. Teflon (fluoroplastic) coating of curvilinear digging shares and track roller provides stable operating procedure up to 26% soil moisture.

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