User's Sleep Research Articles

Advancing mental health predictions through sleep posture analysis: a stacking ensemble learning approach

Sleep posture is closely related to sleep quality, and can offer insights into an individual’s health. This correlation can potentially aid in the early detection of mental health disorders such as depression and anxiety. Current research focuses on embedding pressure sensors in bedsheets, attaching accelerometers on a subject’s chest, and installing cameras in bedrooms for sleep posture monitoring. However, such solutions sacrifice either the user's sleep comfort or privacy. This study explores the effectiveness of using contactless ultra-wideband (UWB) sensors for sleep posture monitoring. We employed a UWB dataset that is composed of the measurements from 12 volunteers during sleep. A stacking ensemble learning method is introduced for the monitoring of sleep postural transitions, which constitute two levels of learning. At the base-learner level, six transfer learning models (VGG16, ResNet50V2, MobileNet50V2, DenseNet121, VGG19, and ResNet101V2) are trained on the training dataset for initial predictions. Then, the logistic regression is employed as a meta-learner which is trained on the predictions gained from the base-learner to obtain final sleep postural transitions. In addition, a sleep posture monitoring algorithm is presented that can give accurate statistics of total sleep postural transitions. Extensive experiments are conducted, achieving the highest accuracy rate of 86.7% for the classification of sleep postural transitions. Moreover, time-series data augmentation is employed, which improves the accuracy by 13%. The privacy-preserving sleep monitoring solution presented in this paper holds promise for applications in mental health research.

Method and finite element verification of indentation calculation for a novel air-spring mattress: for estimating spinal alignment in sleep postures

PurposeThis study aimed to develop a method to calculate the mattress indentation for further estimating spinal alignment.Design/methodology/approachA universal indentation calculation model is derived based on the system theory, and the deformation characteristics of each component are analyzed by the finite element (FE) model of a partial air-spring mattress under the initial air pressure of 0.01–0.025 MPa. Finally, the calculation error of the model is verified.FindingsThe results indicate that the indentation calculation model could describe the stain of a mattress given the load and the constitutive model of each element. In addition, the FE model of a partial air-spring mattress can be used for further simulation analysis with an error of 1.47–3.42 mm. Furthermore, the deformation of the series system is mainly contributed by the air spring and the components directly in contact with it, while the top component is mainly deflection deformation. In addition, the error of the calculation model is 2.17–5.59 mm on the condition of 0.01–0.025 MPa, satisfying the engineering application. Finally, the supine spinal alignment is successfully extracted from the mattress indentation.Research limitations/implicationsThe limitation of this study is that it needs to verify the practicality of the indentation calculation model for the Bonnier spiral spring mattress. The main feature of the Bonnier spring mattress is that all springs are connected, so the mattress deflection and neighborhood effect are more significant than those of the air-spring mattress. Therefore, the applicability of the model needs to be tested. Moreover, it is worth further research to reduce the deformation error of each component.Practical implicationsAs part of the series of studies on the intelligent air-spring mattress, the indentation-based evaluation method of spinal alignment in sleep postures will be studied for hardness and intelligent regulation based on this study.Social implicationsThe results of this research are ultimately used for the intelligent adjustment of air-spring mattresses, which automatically adjusts the hardness according to the user's sleep postures and spinal alignment, thus maintaining optimal spinal biomechanics. The successful application of this result could improve the sleep health of the general public.Originality/valueBased on the series system theory, an indentation calculation model for mattresses with arbitrary structure is proposed, overcoming the dependence of parameters on materials and their combinations when fitting the Burgers model. Further, the spinal alignment in supine posture is extracted from the indentation, laying a theoretical foundation for further recognition and adjustment of the spinal alignment of the intelligent mattress.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study.

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Social media platforms and sleep problems: a systematic literature review, synthesis and framework for future research

PurposeThis study is a systematic literature review (SLR) on prior research examining the impact of the nocturnal use of social media platforms on a user's sleep, its dimensions and its perceptually allied problems. This SLR aims to curate, assimilate and critically examine the empirical research in this domain.Design/methodology/approachForty-five relevant studies identified from the Scopus and Web of Science (WoS) databases were analyzed to develop a comprehensive research profile, identify gaps in the current knowledge and delineate emergent research topics.FindingsPrior research has narrowly focused on investigating the associations between specific aspects of social media use behavior and sleep dimensions. The findings suggest that previous studies are limited by research design and sampling issues. We highlight the imperative need to expand current research boundaries through a comprehensive framework that elucidates potential issues to be addressed in future research.Originality/valueThe findings have significant implications for clinicians, family members and educators concerning promoting appropriate social media use, especially during sleep latency.

A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals

Sleep is an essential integrant in everyone's daily life. Thereby, it is an important but challenging problem to construct a reliable and stable system, that can monitor user's sleep quality automatically. In this brief, we combine complex network and deep learning to propose a novel Graph-Temporal fused dual-input Convolutional Neural Network (CNN) method to detect sleep stages by using the Sleep-EDF database. Firstly, we segment each single-channel EEG signal into non-overlapping 30s epochs to set up the network. For that, we map each epoch into a Limited Penetrable Visibility Graph (LPVG) and obtain the corresponding Degree Sequence (DS) by calculating the node degree. Finally, the DSs and the 30s EEG epochs are combined as inputs of the novel Graph-Temporal fused dual-input CNN to learn about the graph topology and about the temporal feature representations of the raw data for the purpose of classifying the sleep stages into the two-, three-, four-, five-, and six-state. Notably, the classification accuracy of six-state stage detection is 87.21% and the corresponding Kappa value is 0.80. The results demonstrate the effectiveness of our model structure in detecting sleep states, whereby they further provide a basic strategy for future sleep research.

Flexible Electrodes based Smart Mattress for Non-Contact Cardiac Signals Measurement.

For more convenient use in sleep monitoring than conventional electrocardiogram (ECG) measurement systems, a mattress that could measure ECG signal and ballistocardiogram (BCG) signal through clothes was designed. Three electrodes made of silver fiber conductive fabric were embedded in this mattress, which make it very comfortable and it does not disturb user's sleep. In addition to the mattress, the proposed system contains a signal acquisition module, and a data transmission module and a user interface. To reduce the effect of surrounding electromagnetic radiation to the two signal sensing electrodes, two equipotential shielding layers are placed under them respectively. The proposed system was verified by an experiment of ECG signal and BCG signal measurement, where reference ECG signal was measured by Ag/AgCl electrodes synchronously. The result shows that both ECG signal and BCG signal were measured by the proposed system through clothes successfully. Compared with the reference signal, both R-R intervals and J-J intervals extracted have high correlation coefficients, which are 0.988 and 0.963 respectively, and also have small errors (the root mean square errors are 3.64ms and 6.39ms respectively). Furthermore, the proposed system may be used to measure blood pressure indirectly based on ECG signal and BCG signal measured directly.

Designing of Sleep Quality Meter

A sleep quality meter that uses to judge the quality of the user's sleep. A system that monitors the quality of the sleep of its user. This project could be used to determine the factors that lead to an optimal sleep at an individual level. Maybe This project aims to provide a concrete metric for the quality of sleep.The project consists of two main pieces a receiving unit and a sensor unit. The sensor unit contains a heartbeat sensor, a gyroscope and an accelerometer to measure movement, a conductive stretchy cord to measure breathing rate, and a wireless transceiver to relay all of this information to the receiving unit. Thereceiving unit receives the data from the sensor unit, analyzes it and saves so that the user can look on the quality of their sleep.

Detection of Sleep Apnea Using Sonar Smartphone Technology.

This paper assesses the performance of a new noncontact sensing system based on Sonar technology as a Sleep Disordered Breathing (SDB) screener. The respiration and movements of a subject in bed can be measured via a smartphone placed onto a bedside table equipped with a custom app. The app employs novel proprietary algorithms to identify sleep stages and detect SDB patterns.The SDB screener was trained on a set of 94 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous monitoring via a full polysomnography (PSG) system and a smartphone equipped with the app. An additional fully independent set of 68 recordings, uniformly distributed across SDB severity classes, were held out for independent testing. The performance on the test set is excellent and comparable to other existing ambulatory SDB screeners, with a sensitivity of 94% and specificity of 97%, for a clinical threshold for the Apnea Hypopnea Index (AHI) of 15 events/hour.The technology can easily be adopted to scale, as no purchase of dedicated sensors is needed, providing a much needed low- cost alternative for monitoring and potentially screening of large population segments. Furthermore, the non-invasive, contactless sensing does not interfere with the sleeping habits of the user, facilitating longitudinal assessment. This, in combination with the simultaneous measurement of the user's sleep quality, could provide invaluable insights in the subject's response to SDB therapy and lead to increased patient adherence.

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

The quality of sleep may be a reflection of an elderly individual's health state, and sleep pattern is an important measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novelmulti-parametric sensing system called sleep pattern recognition system (SPRS). This system, equipped with a combination of various non-invasive sensors, can monitor an elderly user's sleep behavior. It accumulates the detecting data from a pressure sensor matrix and ultra wide band (UWB) tags. Based on these two types of complementary sensing data, SPRS can assess the user's sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operateswithout disrupting the users' sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

Reduction in time-to-sleep through EEG based brain state detection and audio stimulation.

We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on user's sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.

Already up? using mobile phones to track & share sleep behavior

Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just one's sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a user's interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor one's sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a user's sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately.