Sleep Posture Research Articles

Pressure-Sensor-Based Sleep Status and Quality Evaluation System

Sleep quality evaluation is a major approach to clinical diagnosis of different types of sleep andmental disturbances.How to render it possible for test subjects to complete sleep status detection and quality assessment in a natural environment is what this paper mainly highlights. Clinically, sleep disturbances are analyzed by the so-called Polysomnography (PSG). However, due to limitations in space and high cost, as well as the fact that test subjects must wear various physiological detection equipment during the test, the collected signal is susceptible to interference due to uncomfortable sensations. As such it usually does not reflect the realistic situation of the subject. For this reason,we propose in this paper a pressure-sensor-based smart mattress to realize sleep status detection and quality evaluation. Regarding sleep posture recognition, a proposed convolutional neural network (CNN) model in conjunction with a pressure distribution image formed by the pressure sensing matrix are applied. With respect to assessing the subjects’ time in bed, a support vectormachine (SVM) is used to determine sitting or lying postures, and further recognize the actual time in bed. As for sleep quality assessment, Fuzzy inference is adopted in this paper base on a set of four predefined sleep parameters. Compared with some of prior arts, the proposed systemdoes not require the test subjects to wear any equipment, and as such the subjects can complete the test in a natural environment. Experimental results show that the accuracy of the proposed SVM classifier for differentiating sitting and lying posture and that of CNN model for the recognition of four different sleep postures can be up to 99.986% and 96.987%, respectively. With the proposed model, precise sleep parameters, including time in bed, number of times of bed-leaving, number of times of body movements all night, standard deviation of time interval between body movements, and sleep posture can be provided. Moreover, the system does not use devices such as microphones or surveillance cameras to collect the data of the test subjects; thus, there is no concern about infringing the privacy of the subjects. It is believed that the system will be of considerable help and serve as an aid to clinical diagnosis of sleep disturbance.

Impact of sleep posture and breathing pattern on soft palate flutter and pharynx vibration in a pediatric airway using fluid–structure interaction

Snoring is a common condition in the general population, and the management of snoring requires a better understanding of its mechanism through a fluid–structure interaction (FSI) perspective. Despite the recent popularity of numerical FSI techniques, outstanding challenges are accurately predicting airway deformation and its vibration during snoring due to complex airway morphology. In addition, there still needs to be more understanding of snoring inhibition when lying on the side, and the possible effect of airflow rates, as well as nose or mouth-nose breathing, on snoring remains to be investigated. In this study, an FSI method verified against in vitro models was introduced to predict upper airway deformation and vibration. The technique was applied to predict airway aerodynamics, soft palate flutter, and airway vibration in four sleep postures (supine, left/right lying, and sitting positions) and four breathing patterns (mouth-nose, nose, mouth, and unilateral nose breathing). It was found that, at given elastic properties of soft tissues, the evaluated flutter frequency of 19.8 Hz in inspiration was in good agreement with the reported frequency of snoring sound in literature. Reduction in flutter and vibrations due to the mouth-nose airflow proportion changes were also noticed when having side-lying and sitting positions. Breathing through the mouth results in larger airway deformation than breathing through the nose or mouth-nose. These results collectively demonstrate the potential of FSI for studying the physics of airway vibration and clarify to some degree the reason for snoring inhibition during sleep postures and breathing patterns.

Minimum space requirements for cattle: An approach based on photographic records.

The space requirements for accommodating cattle during confinement are based on assessment of both the physical space occupied by the animals and that needed for essential behaviours. The aim of this study was to estimate the area (m2 ) occupied by cattle of different liveweights in the static standing (ST), recumbency (SR) and sleeping (SL) postures. The study was conducted in Chile. The area (m2 ) occupied by 26 female beef cross cattle with liveweights between 45 and 642kg was estimated in the ST, SR and SL postures through planimetry using overhead photographs. The allometric coefficient k value was obtained for each posture using linear regression equations from the measured weight (kg0.66 ) and area occupied. Less but more variable space was occupied by each individual in the ST posture compared to SR or SL posture, with clear correlations between weight and occupied area. The k values obtained were 0.014 (ST), 0.023 (SR) and 0.021 (SL), which are all less than international recommendations for the same postures. The methodology needs refinement to reduce the distance to the object optical effect in the different postures. Further work is also needed to determine additional space requirements for behavioural needs and movements associated with each posture and change of posture, depending on the type of confinement. We provide a scientific basis on which to objectively establish absolute minimum static space requirements for cattle of different breeds, types, weights and anatomical conformations and calculate k values.

Dual ultra-wideband (UWB) radar-based sleep posture recognition system: Towards ubiquitous sleep monitoring

Dual ultra-wideband (UWB) radar-based sleep posture recognition system: Towards ubiquitous sleep monitoring

Method of recognizing sleep postures based on air pressure sensor and convolutional neural network: For an air spring mattress

The present study aimed to develop a sleep postures recognition system based on the hardness adjustment system for a specific air spring mattress. To the end, an air spring mattress prototype and its embedded system was manufactured. Then the supine and lateral postures were defined, and the sleep posture images generated by the relative change rate of air pressure matrix were filtered. At last, a convolutional neural network (CNN) model was proposed and analyzed by ablation experiment. Furthermore, the CNN model was compared with a CNN-SVM fusion model and a ResNet50 model to valid the performance. The results indicate that it is feasible to define sleep postures with the air pressure, and the images smoothed by a Gaussian filter contains significant features. The F1-score of the CNN model determined by the ablation experiment is 0.981, while the F1-score values of the CNN-SVM fusion model and the ResNet50 model are 0.932 and 0.954, respectively. Therefore, the generalization ability of the CNN model proposed outperformed the other two. Finally, the F1-score of the SSA-CNN model optimized by Sparrow Search Algorithm (SSA) increased to 0.992. It concludes that sleep posture recognition can be achieved using only the inherent structure of the air spring mattress without additional sensors, reducing the cost and complexity of the system. In addition, the air pressure signal can be processed by the proposed CNN model to recognize sleep postures with a high accuracy.

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

Sleep posture one-shot learning framework based on extremity joint kinematics: In-silico and in-vivo case studies

Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e.g. brain waves). Wearable sensors with embedded inertial measurement units have been used for sleep posture classification; nonetheless, previous works consider only few (commonly four) postures, which are inadequate for advanced clinical assessments. Moreover, posture learning algorithms typically require longitudinal data collection to function reliably, and often operate on raw inertial sensor readings unfamiliar to clinicians. This paper proposes a new framework for sleep posture classification based on a minimal set of joint angle measurements. The proposed framework is validated on a rich set of twelve postures in two experimental pipelines: computer animation to obtain synthetic postural data, and human participant pilot study using custom-made miniature wearable sensors. Through fusing raw geo-inertial sensor measurements to compute a filtered estimate of relative segment orientations across the wrist and ankle joints, the body posture was characterised in a way comprehensible to medical experts. The proposed sleep posture learning framework offers plug-and-play posture classification by capitalising on a novel kinematic data augmentation method that requires only one training example per posture. Additionally, a new metric together with data visualisations were employed to extract meaningful insights from the postures dataset, demonstrate the added value of the data augmentation method, and explain the classification performance. The proposed framework attained promising overall accuracy as high as 100% on synthetic data and 92.7% on real data, on par with state of the art data-hungry algorithms available in the literature.

Relation Between Sleep Position and Intraocular Pressure in People with Glaucoma

Glaucoma still be the second most common cause of blindness in the world. Glaucoma is a group of neuropathic diseases of the optic nerve which is characterized by the extraction of the optic disc (cupping) may causes visual disturbances. Intraocular pressure (IOP) is the most influential factor of glaucoma and affected by various factors, especially body position during sleep. Sleep composed of REM sleep and NREM sleep is related to muscle tone and psychological activity that has impact for sleep posture. The increase of intraocular pressure is directly proportional to the increase of episcleral venous pressure. The increase of IOP in prone position is probably due to an increase in choroidal vascular volume and increase of episcleral venous pressure, whereas in the lateral decubitus position the increase in IOP is caused by compression of the jugular veins when using a pillow. IOP rises when move to the lateral decubitus position in the dependent eye was 1 mmHg and in the non-dependent eye was 2 mmHg. The angle of the head to the bed has an effect on increasing IOP, in the vertical head-down position there can be an increase of 70% due to the force of gravity, whereas in patients who sleep with their head at a higher angle to the mattress, the IOP will decrease compared to the horizontal sleeping position.

Transferable Self-Supervised Instance Learning for Sleep Recognition

Although the importance of sleep is increasingly recognized, the lack of general and transferable algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. A deep understanding of the sleep posture, state, or stage is the premise of diagnosing and treating sleep diseases. At present, most existing methods draw support from supervised learning to monitor the whole sleep process. However, in the absence of sufficient labeled sleep data, it is difficult to guarantee the reliability of sleep recognition networks. To solve this problem, we propose a transferable self-supervised instance learning model for three sleep recognition tasks, i.e., sleep posture, state, and stage recognition. Firstly, a SleepGAN is designed to generate sleep data, and then, we combine enough self-supervised rotating sleep data and original data for non-parametric classification at the instance-level, finally, different sleep postures, states, or stages can be distinguished precisely. The proposed model can be applied to multimodal sleep data such as signals and images, and makeup for the inaccuracy caused by insufficient data, and can be transferred to sleep datasets of different sizes. The experimental results show that our algorithm for the physiological changes in the sleep process is superior to several state-of-the-art studies, which may be helpful to promote the intelligence of sleep assessment and monitoring.

Influence of Nasal Cavity Dimension and Sleep Posture on the Severity of Obstructive Sleep Apnea

Background and Objectives The aim of this study was to evaluate the effect of sleep position and nasal cavity dimension according to minimal cross-sectional area (MCA) on the severity of obstructive sleep apnea (OSA).Subjects and Method This study enrolled 528 consecutive patients who completed overnight polysomnography (PSG) and acoustic rhinometry. Positional sleep time and apnea-hypopnea index (AHI) were compared between the right and left lateral sleep positions (RLSP vs. LLSP), and between the wide and narrow lateral side sleep position (WLSP vs. NLSP) according to MCA.Results The sleep time was longer for LSP than for WLSP (20.35%±19.69% and 15.92%±16.35%, respectively) with a statistically significant difference (<i>p</i>=0.001). However, the AHI was not significantly different between the two groups. The sleep time was longer for RLSP than for LLSP (20.65%±19.31% and 15.39%±16.05%, respectively) with a statistically significant difference (<i>p</i><0.001). In the RLSP-dominant group, there were fewer left nasal cavity narrowed patients than right nasal cavity narrowed patients (91 vs. 129, respectively). Furthermore, in the LLSP-dominant group, there were fewer right nasal cavity narrowed patients than left nasal cavity narrowed patients (60 vs. 85, respectively, <i>p</i>=0.001). However, we found that the AHI value was not significantly different according to sleep posture and nasal cavity dimension.Conclusion Snoring patients preferred RLSP to LLSP, and preferred to sleep on the lateral side of the narrow nasal cavity. The OSA severity was not different according to sleep position and nasal cavity dimension.

Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study.

Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy.

Real-Time and Cost-Effective Smart Mat System Based on Frequency Channel Selection for Sleep Posture Recognition in IoMT

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.

Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks

BackgroundCapacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition.MethodsTwo sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced.ResultsIn the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment.ConclusionsThe results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

SleepCon: Sleeping Posture Recognition Model using Convolutional Neural Network

Recognition of sleep patterns and posture has sparked interest in various clinical applications. Sleep postures can be monitored autonomously and constantly to provide useful information for decreasing health risks. Existing systems mostly use images to train the model to learn based on many sensors. For example, a camera, pressure sensor, and electrocardiogram. In this study, a model (named as SleepCon) was designed using deep learning, which will have the capability to train with any threshold image obtained from any sensor. This paper presented a system where data was obtained from a camera installed on the top of a mattress. The camera located the movement of the body posture on the mattress while the subject was lying down on the mattress. In doing so, CNN and other pre-processed steps took place to collect data and then analyze the data to recognize different sleep postures. This model was stored for use in real-time applications. The system can recognize the three major postures, i.e., left, right, and supine. A real-time application is also developed and operates the stored SleepCon model through an accompanying desktop application for detecting the posture live. The accuracy of classification was greater than 90%, while the actual application accuracy was 100% after carrying out the experiment on the SleepCon model. Doi: 10.28991/ESJ-2023-07-01-04 Full Text: PDF

Smart mattress integrated with pressure sensor and IoT functions for sleep apnea detection

Obstructive Sleep Apnea (OSA) is the main reason for sleep-associated respiration disorders, wherein respiration stops and begins evolved again and again at some pointing sleep. Sleep-disordered respiration can affect our common health protection and quality of life. This look targets at detecting sleep posture and sleep-disordered respiration through measuring the sensory reaction of a pressure sensor and respiration microphone sensor set up with mattresses, pillowcases, and positional sleep apnea. Three positions use of pressure sensors were used to analyze. The respiration microphone sensor is used for the duration of sleep and to measure breathing levels. In this work, we have got an advanced sleep monitoring system based on an inattentive method. Internet of Things (IoT) is used to transmit information saved in sensors and displayed in internet applications. Helps us without problems displaying gold coins and gold time periods.

An Improved Sleeping Posture Recognition Using Pressure Sensor Data and Deep Learning

Many chronic diseases, such as cardiovascular disease and sleep disorders, can be diagnosed and treated by assessing the sleeping quality. Sleep posture recognition is an important part of determining sleep quality in sleep research. While most recent studies focus on the classification problem with the number of sleeping postures being equal to or less than ten, this study aims to achieve state-of-the-art results on 17 in-bed position classification. To do this, a spatial pyramid pooling module was added to the top of the EfficientNet B0 model and the contrastive loss and cross-entropy loss functions are combined to act as the main loss function. The random 10% salt and pepper noise are utilized to augment the training data. Experimental results confirm that the proposed approach achieves the best accuracy of 96.01% and outperforms the existing methods. Additionally, we also provide an estimation related to the impact of various combinations of backbone models and loss functions on the performance of the classifier.

A Fully Integrated Flexible Electronic System With Highly Sensitive MWCNTs Piezoresistive Array Sensors for Pressure Monitoring

Pressure ulcers are a common and costly health problem in clinical medicine, caused primarily by continuous vertical pressure on local tissues of the body. Recently, flexible devices have been used to alert pressure ulcers by continuously monitoring the pressure applied to the human skin. Although many pressure sensors provide excellent sensing performance, they still rely on uncomfortable, unstretchable systems with rigid electronics. These devices are mounted on the human body, which hinders continuous pressure monitoring. Herein, we propose a fully integrated flexible electronic system (FIFES) with highly sensitive multi-walled carbon nanotubes (MWNTs) piezoresistive array sensors for pressure monitoring, which accurately achieves external pressure signals acquisition and independently performs processing and wireless transmission. We develop a heat seal connecting technology for integrating the piezoresistive array sensors and its flexible processing circuit, which overcomes the compatibility defect of a rigid circuit board and the flexible sensors. Furthermore, the Cu–Ge metal sputtering and transferring technologies are employed to achieve an extensible circuit. The as-fabricated fully integrated electronic system has excellent flexibility, a rapid response time (the response/recovery time is 0.11/0.08 s, respectively), a wide dynamic range (6–50 kPa), and an excellent sensitivity (1.61 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> for the range under the pressure of 18 kPa, 0.47 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> for the range of 18–50 kPa) attributes to the optimized conical microstructures number. Owing to its excellent performance, the FIFES has extensive applications for pressure monitoring in different human epidermises, such as elbow bending and knee-bending. Also, it has immense potential applications in sleep posture monitoring and pressure ulcer warning.

Detection of Sleep Paralysis by using IoT Based Device and Its Relationship Between Sleep Paralysis And Sleep Quality

When a person wakes up in the middle of the night, they are paralyzed. Despite the fact that most episodes are associated with extreme terror and some might cause clinically significant suffering, little is understood about the experience. This study will analyze existing research on the relationship between sleep paralyses and sleep in general. Many studies have connected poor sleep quality to an increased risk of sleep paralysis. Awake yet unable to act, sleep paralysis occurs. This might happen between awake and sleeping. The problem is approached in three steps: Data collection, data storage, calculation and machine learning prediction of sleep paralysis. The data came from the Smart Device. The dataset has several (independent) and dependent variables (Outcome). This device has been put to the test. Each exam has its own set of features and predicted outcomes. To assess the system's validity, we executed a posture recognition accuracy test. The device was hidden on top of the bed. The controller is in charge of measurement and data collection. Experiments were conducted by collecting pressure data from a patient lying down. The person acted out his sleeping positions on a mat for a while. Machine learning has been used to predict sleep paralysis. By comparing sleep postures to the outcome, we were able to show the link between sleep qualities and sleep paralysis. Machine learning approaches have been used to predict sleep paralysis. Comparing sleeping positions with the results showed the link between sleep quality and sleep paralysis. Sleep paralysis correlates with poor sleep quality. The Random Forest model has the highest accuracy of 91.9 percent in predicting sleep paralysis in the given dataset. SVM with Linear Kernel was 80.49 percent accurate, RBF was 42.68 percent, and Polynomial was 47.56 percent. The accuracy of logistic regression was 76.83 percent. KNN had a dismal performance of 60.98%. Decision Trees and Gradient Boosting both fared well at 85.37 percent.

The Arousal-motor Hypothesis of Dopamine Function: Evidence that Dopamine Facilitates Reward Seeking in Part by Maintaining Arousal

Dopamine facilitates approach to reward via its actions on dopamine receptors in the nucleus accumbens. For example, blocking either D1 or D2 dopamine receptors in the accumbens reduces the proportion of reward-predictive cues to which rats respond with cued approach. Recent evidence indicates that accumbens dopamine also promotes wakefulness and arousal, but the relationship between dopamine’s roles in arousal and reward seeking remains unexplored. Here, we show that the ability of systemic or intra-accumbens injections of the D1 antagonist SCH23390 to reduce cued approach to reward depends on the animal’s state of arousal. Handling the animal, a manipulation known to increase arousal, was sufficient to reverse the behavioral effects of the antagonist. In addition, SCH23390 reduced spontaneous locomotion and increased time spent in sleep postures, both consistent with reduced arousal, but also increased time spent immobile in postures inconsistent with sleep. In contrast, the ability of the D2 antagonist haloperidol to reduce cued approach was not reversible by handling. Haloperidol reduced spontaneous locomotion but did not increase sleep postures, instead increasing immobility in non-sleep postures. We place these results in the context of the extensive literature on dopamine’s contributions to behavior, and propose the arousal-motor hypothesis. This novel synthesis, which proposes that two main functions of dopamine are to promote arousal and facilitate motor behavior, accounts both for our findings and many previous behavioral observations that have led to disparate and conflicting conclusions.