An Intelligent Sensing System for Sleep Motion and Stage Analysis

Bak, Jin U.; Giakoumidis, Nikolaos; Kim, Gagyung; Dong, Haiwei; Mavridis, Nikolaos

doi:10.1016/j.proeng.2012.07.292

Cited by 12 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature suggests there are various methods, approaches, tools, and metric to measure and quantify sleep quality and thermal comfort in isolation. 8,10,[27][28][29]11,[20][21][22][23][24][25][26] But there's no standard method which takes an approach to study these both, emphasizing the lack of a standardized methodology to conduct field trials in real bedrooms. Method The method involves conducting a literature review to identify appropriate indices for measuring sleep quality and thermal comfort of sleeping people.…”

Section: P49 Assessing Sleep Quality and Thermal Comfort In Real Bedr...mentioning

confidence: 99%

P49 Assessing sleep quality and thermal comfort in real bedrooms: towards a standardised methodology

Bhadra,

Beizaee,

Hartescu

et al. 2023

Abstracts

View full text Add to dashboard Cite

Section: P49 Assessing Sleep Quality and Thermal Comfort In Real Bedr...mentioning

confidence: 99%

P49 Assessing sleep quality and thermal comfort in real bedrooms: towards a standardised methodology

Bhadra,

Beizaee,

Hartescu

et al. 2023

Abstracts

View full text Add to dashboard Cite

“…However, in the context of dementia it is not an option to require a patient to wear additional sensors to obtain information about their sleep. A well known and in the past often used indicator to determine sleep quality is the movement during sleep using images [11,12]. With the sleep quality estimation proposed in this paper, we introduce an approach to distinguish between light and deep sleep.…”

Section: Related Workmentioning

confidence: 99%

Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

Siedel

Scheck

Grassi

et al. 2020

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

In recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.

show abstract

“…Noncontact approach usually employs imaging sensors to noninvasively detect sleep behaviors. The methods with microwave [28] and thermal imaging system [29] are advantageous to see through the bed covering to detect body movement. However, these imaging modalities are expensive, not portable, and is unable to perform long-time nocturnal video recording.…”

Section: Noncontact Sensors For Sleep Behavior Analysismentioning

confidence: 99%

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Wang

Chen

2019

Electronics

View full text Add to dashboard Cite

Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method.

show abstract

An Intelligent Sensing System for Sleep Motion and Stage Analysis

Cited by 12 publications

References 5 publications

P49 Assessing sleep quality and thermal comfort in real bedrooms: towards a standardised methodology

P49 Assessing sleep quality and thermal comfort in real bedrooms: towards a standardised methodology

Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Contact Info

Product

Resources

About