Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors

Noncontact respiration monitoring techniques in young children: A scoping review

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children <12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS-2 tool. A total of 19 studies were included. Techniques could be grouped into bed-based methods, microwave radar, video, infrared (IR) cameras, and garment-embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed-based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost-effectiveness, ease-of-use, and widespread availability.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors

Techniques for noninvasively acquiring the vital information of infants and young children are considered very useful in the fields of healthcare and medical care. An unobstructive measurement method for sleeping infants and young children under the age of 6 years using a sheet-type vital sensor with a polyvinylidene fluoride (PVDF) pressure-sensitive layer is demonstrated. The signal filter conditions to obtain the ballistocardiogram (BCG) and phonocardiogram (PCG) are discussed from the waveform data of infants and young children. The difference in signal processing conditions was caused by the physique of the infants and young children. The peak-to-peak interval (PPI) extracted from the BCG or PCG during sleep showed an extremely high correlation with the R-to-R interval (RRI) extracted from the electrocardiogram (ECG). The vital changes until awakening in infants monitored using a sheet sensor were also investigated. In infants under one year of age that awakened spontaneously, the distinctive vital changes during awakening were observed. Understanding the changes in the heartbeat and respiration signs of infants and young children during sleep is essential for improving the accuracy of abnormality detection by unobstructive sensors.

Assessments of perioperative respiratory pattern with non-contact vital sign monitor in children undergoing minor surgery: a prospective observational study

PURPOSE

Nurses routinely assess respiration of hospitalized children; however, respiratory rate measurements are technically difficult due to rapid and small chest wall movements. The aim of this study is to reveal the respiratory status of small children undergoing minor surgery with load cells placed under the bed legs, and to test the hypothesis that respiratory rate (primary variable) is slower immediately after arrival to the ward and recovers in 2 h.

METHODS

Continuous recordings of the load cell signals were performed and stable respiratory waves within the 10 discriminative perioperative timepoints were used for respiratory rate measurements. Apnea frequencies were calculated at pre and postoperative nights and 2 h immediately after returning to the ward after surgery.

RESULTS

Continuous recordings of the load cell signals were successfully performed in 18 children (13 to 119 months). Respiratory waves were appraisable for more than 70% of nighttime period and 40% of immediate postoperative period. There were no statistically significant differences of respiratory rate in any timepoint comparisons (p = 0.448), thereby not supporting the study hypothesis. Respiratory rates changed more than 5 breaths per minute postoperatively in 5 out of 18 children (28%) while doses of fentanyl alone did not explain the changes. Apnea frequencies significantly decreased 2 h immediately after returning to the ward and during the operative night compared to the preoperative night.

CONCLUSION

Respiratory signal extracted from load cell sensors under the bed legs successfully revealed various postoperative respiratory pattern change in small children undergoing minor surgery.

CLINICAL TRAIL REGISTRATION

UMIN (University Hospital Information Network) Clinical Registry: UMIN000045579 ( https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000052039 ).

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

Identifying an Appropriate Area to Facilitate the Cardiorespiratory Measurement during Sleep Monitoring

Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.

Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers.

An accelerometer-derived ballistocardiogram method for detecting heartrates in free-ranging marine mammals

Physio-logging methods, which use animal-borne devices to record physiological variables, are entering a new era driven by advances in sensor development. However, existing datasets collected with traditional bio-loggers, such as accelerometers, still contain untapped eco-physiological information. Here, we present a computational method for extracting heart rate from high-resolution accelerometer data using a ballistocardiogram. We validated our method with simultaneous accelerometer–electrocardiogram tag deployments in a controlled setting on a killer whale (Orcinus orca) and demonstrate the predictions correspond with previously observed cardiovascular patterns in a blue whale (Balaenoptera musculus), including the magnitude of apneic bradycardia and increase in heart rate prior to and during ascent. Our ballistocardiogram method may be applied to mine heart rates from previously collected accelerometery data and expand our understanding of comparative cardiovascular physiology.

Highlighted Article: Validation of a computational method for extracting heart rate in free-ranging cetaceans from high-resolution accelerometer data using a ballistocardiogram.

ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals

As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (E abs) and an averaged relative error (E rel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifi- able respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respi- ratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respira- tory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low- frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare

We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.

Accurate Ballistocardiogram Based Heart Rate Estimation Using an Array of Load Cells in a Hospital Bed

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video

Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3.

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.

Performance Evaluation of Processing Methods for Ballistocardiogram Peak Detection

Several methods are proposed in the literature to detect ballistocardiogram (BCG) peaks. There is a need to narrow these methods down in terms of their performance under similar conditions. This study reports early results from a systematic performance evaluation. To date, we have replicated three methods from the literature and compared their performance using data from five volunteers. A basic cross-correlation approach was also included as a baseline level of performance. The best-performing method had an average peak-detection success rate of 95.0{%, associated with 0.1090 average false alarms per second and 0.0078 s mean standard deviation between real and detected peaks.

Logarithmic Strain Model for Nonlinear Load Cell

General load cells have typically constant sensitivity throughout the measurement range, which is acceptable for common force measurement systems. However, it is not adequate for high-performance control and high-stroke applications such as robotic systems. It is required to have a higher sensitivity in a small force range than that in a large force range. In contrast, for large loading force, it is more important to increase the measurement range than the sensitivity. To cope with these characteristics, the strain curve versus the force measurement should be derived as a logarithmic graph. To implement this nonlinear nature, the proposed load cell is composed of two mechanical components: an activator, which has a curved surface profile to translocate the contact point, and a linear torque measurement unit with a moment lever to measure the loading force. To approximate the logarithmic deformation, the curvature of the activator was designed by an exponential function. Subsequent design parameters were optimized by an evolutionary computation.

Ballistocardiogram signal processing: a review

Across the world, healthcare costs are projected to continue to increase, and the pressure on the healthcare system is only going to grow in intensity as the rate of growth of elderly population increases in the coming decades. As an example, when people age one possible condition that they may experience is sleep-disordered breathing (SDB). SDB, better known as the obstructive sleep apnea (OSA) syndrome, and associated cardiovascular complications are among the most common clinical disorders. The gold-standard approach to accurately diagnose OSA, is polysomnography (PSG), a test that should be performed in a specialist sleep clinic and requires a complete overnight stay at the clinic. The PSG system can provide accurate and real-time data; however, it introduces several challenges such as complexity, invasiveness, excessive cost, and absence of privacy. Technological advancements in hardware and software enable noninvasive and unobtrusive sensing of vital signs. An alternative approach which may help diagnose OSA and other cardiovascular diseases is the ballistocardiography. The ballistocardiogram (BCG) signal captures the ballistic forces of the heart caused by the sudden ejection of blood into the great vessels with each heartbeat, breathing, and body movement. In recent years, BCG sensors such as polyvinylidene fluoride film-based sensors, electromechanical films, strain Gauges, hydraulic sensors, microbend fiber-optic sensors as well as fiber Bragg grating sensors have been integrated within ambient locations such as mattresses, pillows, chairs, beds, or even weighing scales, to capture BCG signals, and thereby measure vital signs. Analysis of the BCG signal is a challenging process, despite being a more convenient and comfortable method of vital signs monitoring. In practice, BCG sensors are placed under bed mattresses for sleep tracking, and hence several factors, e.g., mattress thickness, body movements, motion artifacts, bed-partners, etc. can deteriorate the signal. In this paper, we introduce the sensors that are being used for obtaining BCG signals. We also present an in-depth review of the signal processing methods as applied to the various sensors, to analyze the BCG signal and extract physiological parameters such heart rate and breathing rate, as well as determining sleep stages. Besides, we recommend which methods are more suitable for processing BCG signals due to their nonlinear and nonstationary characteristics.

Contact-free unconstraint respiratory measurements with load cells under the bed in awake healthy volunteers: breath-by-breath comparison with pneumotachography

Rate of respiration is a fundamental vital sign. Accuracy and precision of respiratory rate measurements with contact-free load cell sensors under the bed legs were assessed by breath-by-breath comparison with the pneumotachography technique during two different dynamic breathing tasks in 16 awake human adults resting on the bed. The subject voluntarily increased and decreased the respiratory rate between 4 and 16 breaths/min ( n = 8) and 10 and 40 breaths/min ( n = 8) at every 2 breaths in 6 different lying postures such as supine, left lateral, right lateral, and 30, 45, and 60° sitting postures. Reciprocal phase changes of the upper and lower load cell signals accorded with the respiratory phases indicating respiratory-related shifts of the centroid along the long axis of the bed. Bland-Altman analyses revealed 0.66 and 1.59 breaths/min standard deviation differences between the techniques (limits of agreement: −1.22 to 1.36 and −2.96 to 3.30) and 0.07 and 0.17 breaths/min fixed bias differences (accuracy) (confidence interval: 0.04 to 0.10 and 0.12 to 0.22) for the mean respiratory rates of 10.5 ± 3.7 and 24.6 ± 8.9 breaths/min, respectively, regardless of the body postures on the bed. Proportional underestimation by this technique was evident for respiratory rates >40 breaths/min. Sample breath increase up to 10 breaths improved the precision from 1.59 to 0.26 breaths/min. Abnormally faster and slower respirations were accurately detected. We conclude that contact-free unconstraint respiratory rate measurements with load cells under the bed legs are accurate and may serve as a new clinical and investigational tool.

NEW & NOTEWORTHY Four load cells placed under the bed legs successfully captured a centroid shift during respiration in human subjects lying on a bed. Breath-by-breath comparison of the breaths covering a wide respiratory rate range by pneumotachography confirmed reliability of the contact-free unconstraint respiratory rate measurements by small standard deviations and biases regardless of body postures. Abnormally faster and slower respirations were accurately detected. This technique should be an asset as a new clinical and investigational tool.

Flight Stability Analysis of a Symmetrically-Structured Quadcopter Based on Thrust Data Logger Information

Quadcopter flight stability is achieved when all of the rotors–propellers generate equal thrust in hover and throttle mode. It requires a control system algorithm for rotor speed adjustment, which is related with the translational vector and rotational angle. Even with an identical propeller and speed, the thrusts generated are not necessarily equal on all rotors–propellers. Therefore, this study focuses on developing a data logger to measure thrust and to assist in flight control on a symmetrically-structured quadcopter. It is developed with a four load cells sensor with two-axis characterizations and is able to perform real-time signal processing. The process includes speed adjustment for each rotor, trim calibration, and a proportional integral derivative (PID) control tuning system. In the data retrieval process, a quadcopter was attached with data logger system in a parallel axis position. Various speeds between 1200 rpm to 4080 rpm in throttle mode were analyzed to determine the stability of the resulting thrust. Adjustment result showed that the thrust differences between the rotors were less than 0.5 N. The data logger showed the consistency of the thrust value and was proved by repeated experiments with 118 s of sampling time for the same quadcopter control condition. Finally, the quadcopter flight stability as the result of tuning process by the thrust data logger was validated by the flight controller data.

A real-time approach for heart rate monitoring using a Hilbert transform in seismocardiograms

Heart rate monitoring helps in assessing the functionality and condition of the cardiovascular system. We present a new real-time applicable approach for estimating beat-to-beat time intervals and heart rate in seismocardiograms acquired from a tri-axial microelectromechanical accelerometer. Seismocardiography (SCG) is a non-invasive method for heart monitoring which measures the mechanical activity of the heart. Measuring true beat-to-beat time intervals from SCG could be used for monitoring of the heart rhythm, for heart rate variability analysis and for many other clinical applications. In this paper we present the Hilbert adaptive beat identification technique for the detection of heartbeat timings and inter-beat time intervals in SCG from healthy volunteers in three different positions, i.e. supine, left and right recumbent. Our method is electrocardiogram (ECG) independent, as it does not require any ECG fiducial points to estimate the beat-to-beat intervals. The performance of the algorithm was tested against standard ECG measurements. The average true positive rate, positive prediction value and detection error rate for the different positions were, respectively, supine (95.8%, 96.0% and ≃0.6%), left (99.3%, 98.8% and ≃0.001%) and right (99.53%, 99.3% and ≃0.01%). High correlation and agreement was observed between SCG and ECG inter-beat intervals (r  >  0.99) for all positions, which highlights the capability of the algorithm for SCG heart monitoring from different positions. Additionally, we demonstrate the applicability of the proposed method in smartphone based SCG. In conclusion, the proposed algorithm can be used for real-time continuous unobtrusive cardiac monitoring, smartphone cardiography, and in wearable devices aimed at health and well-being applications.