Bed occupancy monitoring: Data processing and clinician user interface design

Pouliot, Melanie; Joshi, Vilas; Goubran, Rafik; Knoefel, Frank

doi:10.1109/embc.2012.6347315

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Han et al [84] proposed a metric based on the sleeping position and the acceleration of the movements during sleep, multiplying a weight factor associated to the measured position (0 for supine, 1 for prone and 0.5 for left or right) to the normalized value of the acceleration. Pouliot et al [85] analyzed the bed occupancy, specifically the number of times the subject exit the bed during the night and the trend of the bed occupancy, as an indication of the quality of sleep.…”

Section: F Proposed New Metricsmentioning

confidence: 99%

A Review of Approaches for Sleep Quality Analysis

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2019

IEEE Access

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Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term ''sleep quality'' still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject's home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, ''what methods for sleep quality assessment have been developed'' and ''what kind of measures are employed by the devices that have been developed to estimate sleep quality.'' The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art. INDEX TERMS Sleep quality analysis, monitoring devices, home detection.

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Section: F Proposed New Metricsmentioning

confidence: 99%

A Review of Approaches for Sleep Quality Analysis

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2019

IEEE Access

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“…Although bed occupancy information has been collected and interfaces designed to display the information in a meaningful way to health care providers [10], the problem of feasible data processing currently exists as a potential barrier to the adoption of this technology. This paper aims to address this problem by examining the minimum data sampling rate required to calculate accurate and time-efficient measures of bed occupancy.…”

Section: Introductionmentioning

confidence: 99%

“…Pressure sensors installed under the mattress can monitor bed occupancy. The amount of time the person spends in bed, the number of times they get up in the night, and the time of day they sleep are all features of bed occupancy that can be extracted from pressure sensors [10].…”

Section: Introductionmentioning

confidence: 99%

Bed occupancy measurements using under mattress pressure sensors for long term monitoring of community-dwelling older adults

Taylor

Grant

Knoefel

et al. 2013

2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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-There is a growing demand for systems that support independent living into advanced age. Technologies that monitor changes in the amount of time older adults spend in bed have the potential to detect critical changes in mobility and support earlier health intervention. Although under mattress sensors have been used previously, processing algorithms were designed for short term monitoring. The objective of this paper was to develop an algorithm and determine optimal sampling rate to obtain bed occupancy characteristics over the longer term. Under mattress sensors were installed in the home of an older adult and data collected over a 3 month period. A processing algorithm was developed to extract bed occupancy information including time in bed, number of bed exits and time of first morning exit. Data were compared using various sampling rates and processing times. Findings indicate that the ideal down sample time for the application was 5 seconds (0.2Hz) and that computational time requirements could be reduced significantly without sacrificing the ability to accurately measure the total time spent in bed and number of bed exits. Features of bed occupancy were plotted and patterns discovered which may be of interest to health clinicians and sleep researchers.

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“…PSMs have been used to monitor patients at home, in continuing care facilities, and in hospitals for a variety of health conditions. Our lab and others have used pressure sensitive measurement technologies to characterize sleep quality [38], [39], older adult mobility [29], sleep apnea detection [38], [40], and bed exit/re-entries over the course of a night [41], [42].…”

Section: Pressure Sensitive Matsmentioning

confidence: 99%

Analysis of Contact Pressure for Monitoring Body Fluid Distribution

Cohen-McFarlane¹

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Fluid distribution is a key indicator for a variety of diseases that become more prevalent as we age, such as congestive heart failure (CHF) and nocturia. Current methods to monitor fluid distribution rely on infrequent and often self-report measures, which are unreliab le.This thesis aims to develop and evaluate a continuous, unobtrusive, and non-invas ive alternative method based on the use of pressure sensitive mats (PSM) to accurately measure fluid distribution within a patient. We here detail the design of an algorithmic system to monitor changes in fluid distribution over time.A series of four increasingly complex experiments were performed using PSMs.The first three experiments established that PSMs are sufficiently sensitive to detect small variations in mass typical of fluid retention observed in CHF patients, where fluid pools in the ankles while the patient is vertical and redistributes to the bladder over night while horizontal. The final experiment and corresponding algorithm aimed to differentiate when an individual exits the bed to: (1) do nothing, (2) void their bladder, or (3) drink as much water as was comfortable. A method to automatically segment a person's pressure image into regions of interest (ROI) was developed. Based on the results from a single participant in three different postures (prone, supine, and side), the system was able to identify bed exits associated with large voids and drinking events (p < 0.05). Small voids were only reliably detectable in the prone positon (p < 0.01). These results suggest that this system is effective at distinguishing between significant fluid intake and fluid output events, and emphasizes the potential for novel non-invasive fluid distribution monitoring systems to improve traditional healthcare approaches.iii

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Bed occupancy monitoring: Data processing and clinician user interface design

Cited by 10 publications

References 12 publications

A Review of Approaches for Sleep Quality Analysis

A Review of Approaches for Sleep Quality Analysis

Bed occupancy measurements using under mattress pressure sensors for long term monitoring of community-dwelling older adults

Analysis of Contact Pressure for Monitoring Body Fluid Distribution

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