Non-invasive analysis of sleep patterns via multimodal sensor input

Metsis, Vangelis; Kosmopoulos, Dimitrios; Athitsos, Vassilis; Makedon, Fillia

doi:10.1007/s00779-012-0623-1

Cited by 47 publications

(21 citation statements)

References 15 publications

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“…17 for an example of a composite question in the smart phone) [72]. The smartphone application has functions to display the history of the sleep environment and the sleep patterns of the individual [73,74]. Using the data collected from the sensors and answers from the sleep questionnaire, recommendations for improved sleep and adjustment of the sleeping room environment, based on assessment from a medical professional and sleeper needs, can be provided.…”

Section: Smart Sleeping Environmentmentioning

confidence: 99%

Information and communications technologies for elderly ubiquitous healthcare in a smart home

Deen

2015

Pers Ubiquit Comput

201

115

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Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, lowpower electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks, smart wrist-bands or smart belts. Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.

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Section: Smart Sleeping Environmentmentioning

confidence: 99%

Information and communications technologies for elderly ubiquitous healthcare in a smart home

Deen

2015

Pers Ubiquit Comput

201

115

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“…In our context, few works have combined pressure sensors with other image acquisition devices, most of them for posture recognition purposes (Huang et al 2010). In particular, Metsis et al (2014) fused temporally-synchronized pressure maps and depth data from Kinect for sleep monitoring, using template matching, kNN and SVM for posture recognition and HMM for motion classification.…”

Section: Related Workmentioning

confidence: 99%

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Palmero

Esquirol²,

Bayo³

et al. 2016

Int J Comput Vis

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This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.

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“…Our approach of sleep analysis is different than the work in [9,13,15]. The authors used a high-resolution camera in front of the bed for sleep analysis which raises concerns about the privacy.…”

Section: Related Workmentioning

confidence: 99%

“…The system has been evaluated on 20 simulated use-cases for 7 hours. In [15]. the authors used Kinect and contact-based pressure mattress to build a sleep monitoring system.…”

Section: Related Workmentioning

confidence: 99%

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Eldib

Deboeverie

Philips

et al. 2015

Human Behavior Understanding

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Abstract. Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 × 30 pixels) for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10 months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error (MAE) of 22.91 minutes and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights.

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Non-invasive analysis of sleep patterns via multimodal sensor input

Cited by 47 publications

References 15 publications

Information and communications technologies for elderly ubiquitous healthcare in a smart home

Information and communications technologies for elderly ubiquitous healthcare in a smart home

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

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