Sleep disruption and sleep position: Increased wake frequency in supine predicts lateral position preference.

Little is known about the physiological and biomechanical factors that determine individual preferences in lying posture during sleep. This study investigated relationships between position preference and position-specific arousals, awakenings, limb movements and limb movement arousals to explore the mechanisms by which biomechanical factors influence position preference. Forty-one mature-aged adults underwent 2 nights of at-home polysomnography ~2 weeks apart, on a standardised firm foam mattress, measuring nocturnal sleep architecture and position. The lateral supine ratio and restlessness indices specific to lateral and supine positions including limb movement index, limb movement arousal index, arousal index, wake index, respiratory arousal index and apnea-hypopnea index were calculated and analysed via linear mixed-effects regression. In the supine position, all restlessness indices were significantly increased compared with the lateral position, including a 379% increase in respiratory arousals (ß = 7.0, p < 0.001), 108% increase in arousal index (ß = 10.3, p < 0.001) and 107% increase in wake index (ß = 2.5, p < 0.001). Wake index in the supine position increased significantly with more lateral sleep (ß = 1.9, p = 0.0013), and significant correlation between lateral supine ratio polysomnography 1 and lateral supine ratio polysomnography 2 (ß = 0.95, p < 0.001) indicated strong consistency in sleep preference. Overall, the findings suggest that some individuals have low tolerance to supine posture, represented by a comparatively high wake index in the supine position, and that these individuals compensate by sleeping a greater proportion in the lateral position.

A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar.

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.

Determinants of Perceived Comfort: Multi-Dimensional Thinking in Smart Bedding Design.

Sleep quality is an important issue of public concern. This study, combined with sensor application, aims to explore the determinants of perceived comfort when using smart bedding to provide empirical evidence for improving sleep quality. This study was conducted in a standard sleep laboratory in Quanzhou, China, from March to April of 2023. Perceived comfort was evaluated using the Subjective Lying Comfort Evaluation on a seven-point rating scale, and body pressure distribution was measured using a pressure sensor. Correlation analysis was employed to analyze the relationship between perceived comfort and body pressure, and multiple linear regression was used to identify the factors of perceived comfort. The results showed that body pressure was partially correlated with perceived comfort, and sleep posture significantly influenced perceived comfort. In addition, height, weight, and body mass index are common factors that influence comfort. The findings highlight the importance of optimizing the angular range of boards based on their comfort performance to adjust sleeping posture and equalize pressure distribution. Future research should consider aspects related to the special needs of different populations (such as height and weight), as well as whether users are elderly and whether they have particular diseases. The design optimization of the bed board division and mattress softness, based on traditional smart bedding, can improve comfort and its effectiveness in reducing health risks and enhancing health status.

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

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

BACKGROUND: Capacitively 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. METHODS: Two 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. RESULTS: In 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. CONCLUSIONS: The results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

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.

Sleep Pattern Analysis in Unconstrained and Unconscious State.

Sleep accounts for one-third of an individual's life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate.

A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification.

Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data.

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach.

In the hospital, a sleep postures monitoring system is usually adopted to transform sensing signals into sleep behaviors. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost-a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve up to 85 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easy-to-wear wrist sensor can accurately quantify sleep postures after our designed training phase. It is our hope that the design concept of iSleePost can shed some lights on quantifying human sleep postures in the future.

A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions.

Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse- and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.

Heat loss in sleeping garden warblers (Sylvia borin) during migration.

For small songbirds, energy is often a limiting factor during migration and, for this reason, they are forced to alternate nocturnal flights with stopovers to rest and replenish energy stores. Stopover duration has a key role for a successful migration and may have an important impact on fitness. Thus, migrants need to optimize their energy consumption at this stage to reduce their permanence at the site. A recent study has shown that lean individuals reduce their metabolic rate when tucking the head in the feathers during sleep. The underlying mechanism is very likely a reduction in conductance, but the thermoregulatory benefit of the increased insulation has never been quantified yet. Here, we compared heat loss in individual migratory birds while sleeping in different postures. Using a thermal camera and a within-individual approach, we estimated that Garden Warblers can reduce their rate of heat loss by 54% by sleeping with the head tucked in the feathers. This energy saving has a relevant impact on the individual's energy balance because it can account for up to 8.69% of daily energy expenditure during stopover. Our study provides novel and important information to understand the fundamental role of thermoregulatory strategies on bird's energy management.

[A Mattress System of Recognizing Sleep Postures Based on BCG Signal].

Sleep posture recognition is the core index of diagnosis and treatment of positional sleep apnea syndrome. In order to detect body postures noninvasively, we developed a portable approach for sleep posture recognition using BCG signals with their morphological difference. A type of piezo-electric polymer film sensor was applied to the mattress to acquire BCG, the discrete wavelet transform with cubic B-spline was used to extract characteristic parameters and a naive Bayes learning phase was adapted to predict body postures. Eleven healthy subjects participated in the sleep simulation experiments. The results indicate that the mean error obtained from heart rates was 0.04±1.3 beats/min (±1.96 SD). The final recognition accuracy of four basic sleep postures exceeded 97%, and the average value was 97.9%. This measuring system is comfortable and accurate, which can be streamlined for daily sleep monitoring application.

A nationwide, cross-sectional survey on unusual sleep postures and sleep-disordered breathing-related symptoms in people with Down syndrome.

BACKGROUND: People with Down syndrome (DS) often have sleep-disordered breathing (SDB). Unusual sleep postures, such as leaning forward and sitting, are observed in people with DS. This study aimed to clarify the prevalence of unusual sleep postures and their relationships with SDB-related symptoms (SDB-RSs), such as snoring, witnessed apnoea, nocturnal awakening and excessive daytime sleepiness. METHODS: A questionnaire, including demographic characteristics and the presence of unusual sleep postures, as well as SDB-RSs, was completed by 1149 parents of people with DS from Japan. RESULTS: Unusual sleep postures were recorded in 483 (42.0%) people with DS. These participants were significantly younger and had a history of low muscle tone more frequently than people without unusual sleep postures. In all ages, the leaning forward posture was more frequent than sitting. People with DS with unusual sleep postures suffered from SDB-RSs. Those who slept in the sitting posture had more frequent SDB-RSs than did those who slept with the leaning forward posture. Snoring, witnessed apnoea and nocturnal awakening were observed in 73.6, 27.2 and 58.2% of participants, respectively. Snoring increased with aging. Witnessed apnoea was more common in males and in those with hypothyroidism than in females and in those without hypothyroidism. CONCLUSIONS: Our study shows that there is a close relationship between unusual sleep postures and SDB-RSs. We recommend that all people with DS with unusual sleep postures should be checked for the presence of SDB.

Effect of sleep posture on neck muscle activity.

[Purpose] This study investigated the effect of sleep posture on neck muscle activity. [Subjects and Methods] The study recruited 20 healthy subjects, who were positioned in three supine sleeping positions: both hands at sides, both hands on the chest, and dominant hand on the forehead. The activities of the scalene and upper trapezius muscles bilaterally were measured by surface electromyography. [Results] The upper trapezius and scalene muscle activity on the right side was significantly greater in the supine with dominant hand on the forehead position than in the other positions. [Conclusion] Sleep posture is important and prevent neck and shoulder musculoskeletal pain.

Obstructive sleep apnea-related symptoms in Japanese people with Down syndrome.

This study evaluated the prevalence of obstructive sleep apnea-related symptoms and assessed the relationship with obesity or unusual sleep postures in Down syndrome patients in Japan. We obtained the demographic characteristics, sleep postures, and obstructive sleep apnea-related symptoms experienced by 90 people as reported by their caregivers. Although 71% reported snoring and 59% arousals, obstructive sleep apnea-related symptoms were not significantly different between obese and non-obese participants. The youngest age group had the fewest obstructive sleep apnea-related symptoms, especially symptoms of snoring. The odds for arousal, nocturia, and apnea tended to be higher in the unusual sleep-postures group. Unusual sleep postures were most frequent in the group 6-15 years of age. People with Down syndrome might sleep in unusual postures to avoid upper airway obstruction caused by other anatomical factors. For nurses and other health professionals working in mainstream service, it is important to screen all persons with Down syndrome for symptoms suggestive of obstructive sleep apnea, particularly those six years of age and older, and to refer them for further evaluation for sleep disorders.

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals.

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.

A dual fusion recognition model for sleep posture based on air mattress pressure detection.

In order to solve the difficult portability problem of traditional non-invasive sleeping posture recognition algorithms arising from the production cost and computational cost, this paper proposes a sleeping posture recognition model focusing on human body structural feature extraction and integration of feature space and algorithms based on a specific air-spring mattress structure, called SPR-DE (SPR-DE is the Sleep Posture Recognition-Data Ensemble acronym form). The model combines SMR (SMR stands for Principle of Spearman Maximal Relevance) with horizontal and vertical division based on the barometric pressure signals in the human body's backbone region to reconstruct the raw pressure data into strongly correlated non-image features of the sleep postures in different parts and directions and construct the feature set. Finally, the recognit-ion of the two sleep postures is accomplished using the AdaBoost-SVM integrated classifier. SPR-DE is compared with the base and integrated classifiers to verify its performance. The experimental results show that the amount of significant features helps the algorithm to classify different sleeping patterns more accurately, and the f1 score of the SPR-DE model determined by the comparison experiments is 0.998, and the accuracy can reach 99.9%. Compared with other models, the accuracy is improved by 2.9% ~ 7.7%, and the f1-score is improved by 0.029 ~ 0.076. Therefore, it is concluded that the SMR feature extraction strategy in the SPR-DE model and the AdaBoost-SVM can achieve high accuracy and strong robustness in the task of sleep posture recognition in a small area, low-density air-pressure mattress, taking into account the comfort of the mattress structural design and the sleep posture recognition, integrated with the mattress adaptive adjustment system.

Characteristics of sleep-disordered breathing in children with down syndrome - A comparison with typically developing children.

Background: Compared with typically developing control children (CC), children with Down syndrome (DS) frequently exhibit sleep-disordered breathing (SDB) and unusual sleep postures (USPs). No studies have directly compared SDB-related signs and symptoms, SDB-related parameters, and USPs between children with DS and CC. This study aimed to evaluate the prevalences of SDB and USPs in children with DS and CC. Methods: We analyzed SDB-related parameters measured via overnight pulse oximetry and questionnaires administered to parents on SDB-related signs and symptoms, including sleeping postures. Estimated SDB was defined as a 3% oxygen desaturation index (ODI) ≥5 dips/h. Results: Fifty-one children with DS (4-5 years: N = 12, 6-10 years: N = 23, 11-15 years: N = 16) and sixty-three CC (4-5 years: N = 18, 6-10 years: N = 27, 11-15 years: N = 18) were included. The prevalence of estimated SDB and observed USPs was higher in children with DS than in CC (p < 0.0001). Among children aged 11-15 years old, but not those aged 4-5 and 6-10 years old, frequency of arousal and apnea (p = 0.045 and p = 0.01, respectively) were higher in children with DS than in CC. Multivariate analyses showed that DS was associated with SDB-related signs and symptoms, estimated SDB, 3% ODI, average oxygen saturation (SpO2), and nadir SpO2, while USPs were associated only with higher values of SpO2 <90%. Conclusions: Estimated SDB tended to increase in children with DS but decreased in CC with growth. USPs were more frequent in children with DS than in CC, especially in older children. USPs might indicate severe hypoxemia due to SDB in DS.

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.