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

Palmero, Cristina; Esquirol, Jordi; Bayo, Vanessa; Cos, Miquel Àngel; Ahmadmonfared, Pouya; Salabert, Joan; Sánchez, David; Escalera, Sérgio

doi:10.1007/s11263-016-0919-0

Cited by 12 publications

(17 citation statements)

References 48 publications

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“…Although young adults were commonly recruited, López-Torres et al (2008) compared the outcomes obtained for young adults and elderly people, revealing the absence of significant differences between these groups in terms of subjective perception. The works of Esquirol Caussa et al (2017) and Palmero et al (2017) encompassed a wide age range (4–93) of subjects, but the influence of age was not discussed in either case.…”

Section: Resultsmentioning

confidence: 99%

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Sleeping mattress determinants and evaluation: a biomechanical review and critique

Wong

Yan

Lin

et al. 2019

PeerJ

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BackgroundSleeping mattress parameters significantly influence sleeping comfort and health, as reflected by the extensive investigations of sleeping support biomechanics to prevent sleep-related musculoskeletal problems.MethodologyHerein, we review the current trends, research methodologies, and determinants of mattress biomechanics research, summarizing evidence published since 2008. In particular, we scrutinize 18 articles dealing with the development of new designs, recommendation criteria, instruments/methods of spine alignment evaluation, and comparative evaluation of different designs.ResultsThe review demonstrated that mattress designs have strived for customization, regional features, and real-time active control to adapt to the biomechanical features of different body builds and postures. However, the suggested threshold or target values for desirable spine alignment and body pressure distribution during sleep cannot yet be justified in view of the lack of sufficient evidence.ConclusionsIt is necessary to formulate standard objectives and protocols for carrying out mattress evaluation.

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Section: Resultsmentioning

confidence: 99%

“…López-Torres et al (2008) selected four mattresses claimed to cover the full range of firmness, while Low et al (2017) commented that the density of their mattresses was intermediate. Palmero et al (2017) used the subjects’ existing mattresses but did not describe their brand, stiffness, or type.…”

Section: Resultsmentioning

confidence: 99%

Sleeping mattress determinants and evaluation: a biomechanical review and critique

Wong

Yan

Lin

et al. 2019

PeerJ

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“…With the recent developments, wireless sensing has opened the doors for sleep monitoring systems leveraging various sensors, such as audio, image, force and temperature [8][9][10][11][12][13][14][15][16][17][18]. Most of these wireless systems are contact-oriented require wearable sensors worn by the patient to acquire adequate precision levels in a clinical setting.…”

Section: Introductionmentioning

confidence: 99%

“…Force sensors deployed under the mattress top have also been used to detect the heart rate, sleep pattern, snoring, or respiration rate [11][12][13][14][15]. Martinez et al have investigated a wireless sensor system to detect respiratory rate using received signal strength indicator [16].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of the Hypopnea syndrome in the early stage

Yang

Fan

Ren

et al. 2019

Neural Comput & Applic

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Hypopnea syndrome is a chronic respiratory disease that is characterized by repetitive episodes of breathing disruptions during sleep. Hypopnea syndrome is a systemic disease that manifests respiratory problems, however, more than 80% of Hypopnea syndrome patients remain undiagnosed due to complicated polysomnography. Objective assessment of breathing patterns of an individual can provide useful insight into the respiratory function unearthing severity of Hypopnea syndrome. This paper explores a novel approach to detect incognito Hypopnea syndrome as well as provide a contactless alternative to traditional medical tests. The proposed method is based on S-Band sensing, peak detection algorithm and sine function fitting for the observation of breathing patterns and characterization of normal or disruptive breathing patterns for Hypopnea syndrome detection. The proposed system observes the human subject and changes in the channel frequency response caused by Hypopnea syndrome utilizing a wireless link between two monopole antennas, placed 3m apart. Accuracy of the proposed method is established through comparison with commercial respiratory sensor. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate Hypopnea syndrome monitoring in a patient friendly and flexible environment.

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“…A solution adopted by some, is the use of multiple data sources: RGB, depth maps, pressure sensors, etc., designing a tailored solution for the specific task. In [30], Palmero et al presented an automatic sleep system recommendation using RGB, depth and pressure information. Unfortunately, this kind of system, without the use of a body model can be heavily affected by the pose of the subject, and the soft tissue deformation with the pose.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Analyzing the Whole Body Surface Area from a Single View

2017

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We present a virtual reality (VR) framework for the analysis of whole human body surface area. Usual methods for determining the whole body surface area (WBSA) are based on well known formulae, characterized by large errors when the subject is obese, or belongs to certain subgroups. For these situations, we believe that a computer vision approach can overcome these problems and provide a better estimate of this important body indicator. Unfortunately, using machine learning techniques to design a computer vision system able to provide a new body indicator that goes beyond the use of only body weight and height, entails a long and expensive data acquisition process. A more viable solution is to use a dataset composed of virtual subjects. Generating a virtual dataset allowed us to build a population with different characteristics (obese, underweight, age, gender). However, synthetic data might differ from a real scenario, typical of the physician’s clinic. For this reason we develop a new virtual environment to facilitate the analysis of human subjects in 3D. This framework can simulate the acquisition process of a real camera, making it easy to analyze and to create training data for machine learning algorithms. With this virtual environment, we can easily simulate the real setup of a clinic, where a subject is standing in front of a camera, or may assume a different pose with respect to the camera. We use this newly designated environment to analyze the whole body surface area (WBSA). In particular, we show that we can obtain accurate WBSA estimations with just one view, virtually enabling the possibility to use inexpensive depth sensors (e.g., the Kinect) for large scale quantification of the WBSA from a single view 3D map.

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

Cited by 12 publications

References 48 publications

Sleeping mattress determinants and evaluation: a biomechanical review and critique

Sleeping mattress determinants and evaluation: a biomechanical review and critique

Diagnosis of the Hypopnea syndrome in the early stage

A Framework for Analyzing the Whole Body Surface Area from a Single View

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