Validation of non-invasive monitoring device to evaluate sleep quality

Pino, Esteban J.; Moran, Alejandra A.; Paz, Astrid Dorner De la; Aqueveque, Pablo

doi:10.1109/embc.2015.7320242

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Other non-invasive technologies include TagSleep, which uses RFID tags placed near the users [ 72 ]. Non-invasive sensing is also achieved through smartphone sensors [ 64 , 73 , 74 ]. Miniature snap-action switches were also deployed in a SPHERE project for activity recognition [ 75 ].…”

Section: Resultsmentioning

confidence: 99%

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

Nthubu

2021

Healthcare

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The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and ‘bedraggled daisy’ Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.

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Section: Resultsmentioning

confidence: 99%

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

Nthubu

2021

Healthcare

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“…A device based on pressure sensors, implemented on the bed, was developed by Pino et al [93], measuring the body position and the number of respiratory cycles per minute. An algorithm to estimate apneic episodes and the periods out of bed was later added to the device [94].…”

Section: Developed Devices For Sleep Quality Estimation a Devicesmentioning

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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“…Most literature focusing on ambient sensing to monitor cognitive health of behavioural disturbances have reported on technology development and feasibility studies with older adults and PwDs [ 46 , 47 , 48 , 49 ]. The literature has traditionally examined routine-action monitoring such as posture analysis [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ] and safety monitoring [ 57 ], fall detection (e.g., [ 58 , 59 , 60 , 61 , 62 , 63 ], indoor localization (e.g., [ 64 , 65 , 66 ], and wandering (e.g., [ 67 ]). Healthcare professionals’ perspectives on the use of ambient sensing technologies have been explored in a variety of settings, including at a people’s home [ 68 , 69 ], in laboratories [ 70 , 71 ] and in healthcare institutions [ 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

Choukou

Mbabaali

East

2021

IJERPH

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The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.

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Validation of non-invasive monitoring device to evaluate sleep quality

Cited by 10 publications

References 8 publications

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

A Review of Approaches for Sleep Quality Analysis

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

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