An ultrasonic contactless sensor for breathing monitoring

Flexible Ag2Se Thermoelectric Films Enable the Multifunctional Thermal Perception in Electronic Skins

Skin is critical for shaping our interactions with the environment. The electronic skin (E-skin) has emerged as a promising interface for medical devices to replicate the functions of damaged skin. However, exploration of thermal perception, which is crucial for physiological sensing, has been limited. In this work, a multifunctional E-skin based on flexible thermoelectric Ag2Se films is proposed, which utilizes the Seebeck effect to replicate the sensory functions of natural skin. The E-skin can enable capabilities including temperature perception, tactile perception, contactless perception, and material recognition by analyzing the thermal conduction behaviors of various materials. To further validate the capabilities of constructed E-skins, a wearable device with multiple sensory channels was fabricated and tested for gesture recognition. This work highlights the potential for using flexible thermoelectric materials in advanced biomedical applications including health monitoring and smart prosthetics.

Human Respiration Rate Measurement with High-Speed Digital Fringe Projection Technique

This paper proposes a non-contact continuous respiration monitoring method based on Fringe Projection Profilometry (FPP). This method aims to overcome the limitations of traditional intrusive techniques by providing continuous monitoring without interfering with normal breathing. The FPP sensor captures three-dimensional (3D) respiratory motion from the chest wall and abdomen, and the analysis algorithms extract respiratory parameters. The system achieved a high Signal-to-Noise Ratio (SNR) of 37 dB with an ideal sinusoidal respiration signal. Experimental results demonstrated that a mean correlation of 0.95 and a mean Root-Mean-Square Error (RMSE) of 0.11 breaths per minute (bpm) were achieved when comparing to a reference signal obtained from a spirometer.

Development of Embroidery-Type Sensor Capable of Detecting Respiration Using the Capacitive Method

This study presents a respiration sensor that is dependent on a parallel capacitor, including connection lines and electrodes embroidered on textiles. First, characterizations of the respiration capacitor using a silver thread, including a combination of porous Eco-flex simulating air in the lungs due to respiration, were evaluated using an LCR meter. Second, the effects of air gaps on the detection of respiration motions according to the change in electrode distance under pressure were presented. The data values were measured from 1 to 300 kHz using an LCR meter and dielectric test fixture. Third, actual breathing was examined in four patterns: normal breathing, deep breathing, hyperventilation, and apnea. The test was performed after fabricating a clothing-type breathing sensor. Finally, the change in capacitance for actual respiration was determined by wearing a clothing-type respiration sensor based on the data collected. The effectiveness of the respiration sensor was demonstrated by measuring it to discern all waveforms, cycles, and ranges associated with the breathing pattern.

Accuracy and reliability of the optoelectronic plethysmography and the heart rate systems for measuring breathing rates compared with the spirometer

Measuring breathing rates without a mouthpiece is of interest in clinical settings. Electrocardiogram devices and, more recently, optoelectronic plethysmography (OEP) methods can estimate breathing rates with only a few electrodes or motion-capture markers placed on the patient. This study estimated and compared the accuracy and reliability of three non-invasive devices: an OEP system with 12 markers, an electrocardiogram device and the conventional spirometer. Using the three devices simultaneously, we recorded 72 six-minute epochs on supine subjects. Our results show that the OEP system has a very low limit of agreement and a bias lower than 0.4% compared with the spirometer, indicating that these devices can be used interchangeably. We observed comparable results for electrocardiogram devices. The OEP system facilitates breathing rate measurements and offers a more complete chest-lung volume analysis that can be easily associated with heart rate analysis without any synchronisation process, for useful features for clinical applications and intensive care.

A Low-Cost Flexible Perforated Respiratory Sensor Based on Platinum for Continuous Respiratory Monitoring

Monitoring sleep conditions is of importance for sleep quality evaluation and sleep disease diagnosis. Accurate respiration detection provides key information about sleep conditions. Here, we propose a perforated temperature sensor that can be worn below the nasal cavity to monitor breath. The sensing system consists of two perforated temperature sensors, signal conditioning circuits, a transmission module, and a supporting analysis algorithm. The perforated structure effectively enhances the sensitivity of the system and shortens the response time. The sensor's response time is 0.07 s in air and sensitivity is 1.4‱°C^-1. The device can achieve a monitoring respiratory temperature range between normal room temperature and 40 °C. The simple and standard micromachining process ensures low cost and high reproducibility. We achieved the monitoring of different breathing patterns, such as normal breathing, panting, and apnea, which can be applied to sleep breath monitoring and exercise information recording.

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene’s test, the Kruskal–Wallis test, and Dunn’s post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.

Free-Breathing Liver Magnetic Resonance Imaging With Respiratory Frequency-Modulated Continuous-Wave Radar-Trigger Technique: A Preliminary Study

Purpose

The aim of this study is to evaluate the performance of free-breathing liver MRI with a novel respiratory frequency-modulated continuous-wave radar-trigger (FT) technique on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) for both healthy volunteers and patients in comparison to navigator-trigger (NT) and belt-trigger (BT) techniques.

Methods

In this prospective study, 17 healthy volunteers and 23 patients with known or suspected liver diseases were enrolled. Six sequences (T2WI and DWI with FT, NT, and BT techniques) were performed in each subject. Quantitative evaluation and qualitative assessment were analyzed by two radiologists. Overall image quality, blurring, motion artifacts, and liver edge delineations were rated on a 4-point Likert scale. The liver and lesion signal-to-noise ratio (SNR), the lesion-to-liver contrast-to-noise ratio (CNR), as well as the apparent diffusion coefficient (ADC) value were quantitatively calculated.

Results

For volunteers, there were no significant differences in the image quality Likert scores and quantitative parameters on T2WI and DWI with three respiratory-trigger techniques. For patients, NT was superior to other techniques for image quality on T2WI; conversely, little difference was found on DWI in qualitative assessment. The mean SNR of the liver on T2WI and DWI with BT, NT, and FT techniques was similar in patients, which is in line with volunteers. FT performed better in terms of higher SNR (705.13 ± 434.80) and higher CNR (504.41 ± 400.69) on DWI at b50 compared with BT (SNR: 651.83 ± 401.16; CNR:429.24 ± 404.11) and NT (SNR: 639.41 ± 407.98; CNR: 420.64 ± 416.61) (p < 0.05). The mean ADC values of the liver and lesion with different techniques in both volunteers and patients showed non-significant difference.

Conclusion

For volunteers, the performance of T2WI as well as DWI with three respiratory-trigger techniques was similarly good. As for patients, FT-DWI is superior to BT and NT techniques in terms of higher lesion SNR and CNR at b50.

New Paths in Respiratory Sleep Medicine: Consumer Devices, e-Health, and Digital Health Measurements

Sleep health and tracking sleep with contemporary wearables have become more popular. Sleep disorders, in particular, sleep-disordered breathing, have a higher prevalence than estimated previously. Many patients with apnea and hypopnea events suffer whereas others do not report complaints or show cardiovascular consequences. Assessment with wearables may support efforts to distinguish which type of apnea is related to aging and which to cardiovascular comorbidities. Innovative methods offer smart solutions for problems that are insufficiently addressed. Telemedical concepts help bring patients to sleep medicine expertise at an early stage. To use these methods clinically, they must be certified as medical devices.

Complex-Valued Wavelet Spectrum Analysis of Respiratory Conditions and Its Feasibility in the Detection of Low-Functional Respiration

Respiratory monitoring is a significant issue to reduce patient risks and medical staff labor in postoperative care and epidemic infection, particularly after the COVID-19 pandemic. Oximetry is widely used for respiration monitoring in the clinic, but it sometimes fails to capture a low-functional respiratory condition even though a patient has breathing difficulty. Another approach is breathing-sound monitoring, but this is unstable due to the indirect measurement of lung volume. Kobayashi in our team is developing a sensor measuring temporal changes in lung volume with a displacement sensor attached across the sixth and eighth ribs. For processing these respiratory signals, we propose the combination of complex-valued wavelet transform and the correlation among spectrum sequences. We present the processing results and discuss its feasibility to detect a low-functional condition in respiration. The result for detecting low-functional respiration showed good performance with a sensitivity of 0.88 and specificity of 0.88 to 1 in its receiver operating characteristic (ROC) curve.

Contact and Remote Breathing Rate Monitoring Techniques: A Review

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning

Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available.

Fiber optic Fabry-Perot sensor that can amplify ultrasonic wave for an enhanced partial discharge detection

Ultrasonic wave is a powerful tool for many applications, such as structural health monitoring, medical diagnosis and partial discharges (PDs) detection. The fiber optic extrinsic Fabry–Perot interferometric (EFPI) sensor has become an ideal candidate for detecting weak ultrasonic signals due to its inherent advantages, and each time with a performance enhancement, it can bring great application potential in broadened fields. Herein, an EFPI ultrasonic sensor for PDs detection is proposed. The sensing diaphragm uses a 5-μm-thickness and beam-supported structure to improve the responsive sensitivity of the sensor at the resonant frequency. Furthermore, the ability of the sensor to detect characteristic ultrasonic signal of PDs is further enhanced by assembling a Fresnel-zone-plate (FZP)-based ultrasonic lens with the sensing probe to amplify the ultrasonic wave before it excites the sensing diaphragm. The final testing results show that the originally developed sensor owns the sensitivity of − 19.8 dB re. 1 V/Pa at resonant frequency. While, when the FZP is assembled with the probe, the sensitivity reaches to − 12.4 dB re. 1 V/Pa, and leads to a narrower frequency band, which indicates that the proposed method has a great potential to enhance the detection ability of sensor to characteristic ultrasonic wave of PDs.

Low Power Contactless Bioimpedance Sensor for Monitoring Breathing Activity

An electronic circuit for contactless detection of impedance changes in a tissue is presented. It operates on the principle of resonant frequency change of the resonator having the observed tissue as a dielectric. The operating frequency reflects the tissue dielectric properties (i.e., the tissue composition and on the tissue physiological changes). The sensor operation was tested within a medical application by measuring the breathing of a patient, which was an easy detectable physiological process. The advantage over conventional contact bioimpedance measurement methods is that no direct contact between the resonator and the body is required. Furthermore, the sensor's wide operating range, ability to adapt to a broad range of measured materials, fast response, low power consumption, and small outline dimensions enables applications not only in the medical sector, but also in other domains. This can be extended, for example, to food industry or production maintenance, where the observed phenomena are reflected in dynamic dielectric properties of the observed object or material.

Fetal Movement Counting Using Optical Fibre Sensors

Daily fetal movement counting based on maternal perception is widely deployed to monitor fetal wellbeing. However, the counting performed by the mother is prone to errors for various reasons. There are limited devices on the market that can provide reliable and automatic counting. This paper presents a prototype of a novel fetal movement monitoring device based on fibre Bragg grating sensors. Deformation of the skin caused by a fetal movement can lead to a change of the strain and stress on the optical fibre sensors, therefore can induce distortions to the breathing pattern of the mother. In the study data was gathered by the sensors through strain measurement and was post-processed using independent component analysis (ICA) and high-pass filtering to show the instances of the fetal movements. Information gathered during user trials with the prototype suggests that the system detects significantly higher numbers of fetus movements than that observed based on the mother's perception. Among the various techniques available for fetal movement monitoring, fibre optic sensing provides many advantages including multiplex capability, flexibility and minimal size, making the concept an attractive solution for reliable monitoring of antenatal fetal movements.

Surface imaging for real-time patient respiratory function assessment in intensive care

PURPOSE

Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients.

METHODS

The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital.

RESULTS

The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients.

CONCLUSION

This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

A Review of the State of the Art in Non-Contact Sensing for COVID-19

COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

A Novel, Contactless, Portable “Spot-Check” Device Accurately Measures Respiratory Rate

Respiratory rate (RR) is an important vital sign used in the initial and ongoing assessment of acutely ill patients. It is also used as a predictor of serious deterioration in a patient's clinical condition. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation, and temperature. Although devices which measure RR exist, none has entered everyday clinical practice. We have developed a contactless portable respiratory rate monitor (CPRM) and evaluated the agreement in respiratory rate measurements between existing methods and our new device. The CPRM uses thermal anemometry to measure breath signals during inspiration and expiration. RR data were collected from 52 healthy adult volunteers using respiratory inductance plethysmography (RIP) bands (established contact method), visual counting of chest movements (established noncontact method), and the CPRM (new method), simultaneously. Two differently shaped funnel attachments to the CPRM were evaluated for each volunteer. Data showed a good agreement between measurements from the CPRM and the gold standard RIP, with intraclass correlation coefficient (ICC): 0.836, mean difference 0.46 and 95% limits of agreement of −5.90 to 6.83. When separate air inlet funnels of the CPRM were analyzed, stronger agreement was seen with an elliptical air inlet; ICC 0.908, mean difference 0.37 with 95% limits of agreement −4.35 to 5.08. A contactless device for accurately and quickly measuring respiratory rate will be an important triage tool in the clinical assessment of patients. More testing is needed to explore the reasons for outlying measurements and to evaluate in the clinical setting.

The future of sleep health: a data-driven revolution in sleep science and medicine

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Sleep Apnea Detection with Polysomnography and Depth Sensors

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.

Locating Ultrasonic Signals Employing MEMS-On-Fiber Sensors

Sound sensing finds wide applications in various fields, such as underwater detection, structural health monitoring, and medical diagnosis, to name just a few. Based on our previously developed MEMS-on-fiber sensors, showing the advantages of low cost, small volume, and high performance, a three-dimensional ultrasonic localization system employing four such sensors was established in this work. A time difference of arrival (TDOA) algorithm was utilized to analyze the acquired data and then calculate the accurate position of the ultrasonic signal source. Plenty of practical measurements were performed, and the derived localization deviation in the region of 2 m × 2 m × 1 m was about 2-5 mm. Outside this region, the deviation tended to increase due to the directional sensitivity existing in these sensors. As a result, for a more accurate localization requirement, more sensing probes are needed in order to depict a completely suitable application situation for MEMS technology.

Design and Evaluation of a Non-ContactBed-Mounted Sensing Device for AutomatedIn-Home Detection of Obstructive Sleep Apnea:A Pilot Study

We conducted a pilot study to evaluate the accuracy of a custom built non-contact pressure-sensitive device in diagnosing obstructive sleep apnea (OSA) severity as an alternative to in-laboratory polysomnography (PSG) and a Type 3 in-home sleep apnea test (HSAT). Fourteen patients completed PSG sleep studies for one night with simultaneous recording from our load-cell-based sensing device in the bed. Subjects subsequently installed pressure sensors in their bed at home and recorded signals for up to four nights. Machine learning models were optimized to classify sleep apnea severity using a standardized American Academy of Sleep Medicine (AASM) scoring of the gold standard studies as reference. On a per-night basis, our model reached a correct OSA detection rate of 82.9% (sensitivity = 88.9%, specificity = 76.5%), and OSA severity classification accuracy of 74.3% (61.5% and 81.8% correctly classified in-clinic and in-home tests, respectively). There was no difference in Apnea Hypopnea Index (AHI) estimation when subjects wore HSAT sensors versus load cells (LCs) only (p-value = 0.62). Our in-home diagnostic system provides an unobtrusive method for detecting OSA with high sensitivity and may potentially be used for long-term monitoring of breathing during sleep. Further research is needed to address the lower specificity resulting from using the highest AHI from repeated samples.

Physiological Validation of an Airborne Ultrasound Based Surface Motion Camera for a Contactless Characterization of Breathing Pattern in Humans

Characterizing the breathing pattern in naturally breathing humans brings important information on respiratory mechanics, respiratory muscle, and breathing control. However, measuring breathing modifies breathing (observer effect) through the effects of instrumentation and awareness: measuring human breathing under true ecological conditions is currently impossible. This study tested the hypothesis that non-contact vibrometry using airborne ultrasound (SONAR) could measure breathing movements in a contactless and invisible manner. Thus, first, we evaluated the validity of SONAR measurements by testing their interchangeability with pneumotachograph (PNT) measurements obtained at the same time. We also aimed at evaluating the observer effect by comparing breathing variability obtained by SONAR versus SONAR-PNT measurements. Twenty-three healthy subjects (12 men and 11 women; mean age 33 years - range: 20-54) were studied during resting breathing while sitting on a chair. Breathing activity was described in terms of ventilatory flow measured using a PNT and, either simultaneously or sequentially, with a SONAR device measuring the velocity of the surface motion of the chest wall. SONAR was focused either anteriorly on the xiphoid process or posteriorly on the lower part of the costal margin. Discrete ventilatory temporal and volume variables and their coefficients of variability were calculated from the flow signal (PNT) and the velocity signal (SONAR) and tested for interchangeability (Passing-Bablok regression). Tidal volume (VT) and displacement were linearly related. Breathing frequency (BF), total cycle time (TT), inspiratory time (TI), and expiratory time (TE) met interchangeability criteria. Their coefficients of variation were not statistically significantly different with PNT and SONAR-only. This was true for both the anterior and the posterior SONAR measurements. Non-contact vibrometry using airborne ultrasound is a valid tool for measuring resting breathing pattern.

A System for Monitoring Breathing Activity Using an Ultrasonic Radar Detection with Low Power Consumption

Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.

An Ultrasound-Based Biomedical System for Continuous Cardiopulmonary Monitoring: A Single Sensor for Multiple Information

Biomedical wearable sensors enable long-term monitoring applications and provide instantaneous diagnostic capabilities. Physiological monitoring can help in both the diagnosis and the ongoing treatment of a vast number of cardiovascular and pulmonary diseases such as hypertension, dysrhythmia, and asthma. In this paper, we present a system capable of monitoring several vital signals and physiological variables that determine the cardiopulmonary activity status. We explore direct measurements of multiple vital parameters with only one sensor and without special constraints. The system employs a PZT-4 piezo transducer stimulated by a suitable analog front end. The system both generates pulsed ultrasound waves at 1 MHz and amplifies reflected echoes to track internal organ motions, mainly that of the heart apex. According to the respiratory motion of the heart, the proposed system provides respiratory and heart cycles information. Promising results were obtained from six subjects with an average accuracy of 96.7% in heartbeats per minute measurement, referenced to a commercial photoplethysmography sensor. It also exhibits 94.5% sensitivity and 94.0% specificity in respiration detection compared to a spirometer signal as a reference.

Exploratory Study to Evaluate Respiratory Rate Using a Thermal Imaging Camera

BACKGROUND

Respiratory rate is a vital physiological measurement used in the immediate assessment of unwell children and adults. Convenient electronic devices exist for the measurement of pulse, blood pressure, oxygen saturation, and temperature. Although devices which measure respiratory rate exist, none have entered everyday clinical practice for acute assessment of children and adults. An accurate and practical device which has no physical contact with the patient is important to ensure readings are not affected by distress caused by the assessment method.

OBJECTIVE

The aim of this study was to evaluate the use of a thermal imaging method to monitor the respiratory rate in children and adults.

METHODS

Facial thermal images of adult volunteers and children undergoing elective polysomnography were included. Respiration was recorded for at least 2 min with the camera positioned 1 m from the subject's face. Values obtained using the thermal imaging camera were compared with those obtained from contact methods such as the nasal thermistor, respiratory inductance plethysmography, nasal airflow, and end tidal CO2.

RESULTS

A total of 61 subjects, including 41 adults (age range 27-46 years) and 20 children (age range 0.5-18 years) were enrolled. The correlation between the respiratory rate measured using thermal imaging and the contact method was r = 0.94. Sequential refinements to the thermal imaging algorithms resulted in the ability to perform real-time measurements and an improvement of the correlation to r = 0.995.

CONCLUSION

This exploratory study shows that thermal imaging-derived respiratory rates in children and adults correlate closely with the best performing standard method. With further refinements, this method could be implemented in both acute and chronic care in children and adults.

Ultrasound Sensors for Diaphragm Motion Tracking: An Application in Non-Invasive Respiratory Monitoring

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating the function of the diaphragm and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activity when the sensor is placed in the zone of apposition (ZOA). This system allows for direct measurements with only one ultrasound PZT5 piezo transducer. The system generates pulsed ultrasound waves at 2.2 MHz and amplifies reflected echoes. An added benefit of this system is that due to its design, the respiratory signal is less subject to motion artefacts. Promising results were obtained from six subjects performing six tests per subject with an average respiration detection sensitivity and specificity of 84% and 93%, respectively. Measurements were compared to a gold standard commercial spirometer. In this study, we also compared our measurements to other conventional methods such as inertial and photoplethysmography (PPG) sensors.

Compact Eucapnic Voluntary Hyperpnoea Apparatus for Exercise-Induced Respiratory Disease Detection

Eucapnic voluntary hyperpnoea (EVH) challenge provides objective criteria for exercise-induced asthma (EIA) or exercise-induced bronchoconstriction (EIB), and it was recommended to justify the use of inhaled β₂-agonists by athletes for the Olympics. This paper presents the development of a compact and easy-to-use EVH apparatus for assessing EIB in human subjects. The compact apparatus has been validated on human subjects and the results have been compared to the conventional EVH system. Twenty-two swimmers, including eleven healthy subjects and eleven subjects who had been physician-diagnosed with asthma, were recruited from sport and recreation centers throughout Auckland, New Zealand. Each subject performed two EVH challenge tests using the proposed breathing apparatus and the conventional Phillips EVH apparatus on separate days, respectively. Forced expiratory volume in one second (FEV₁) was measured before and after the challenges. A reduction in FEV₁ of 10% or more was considered positive. Of the eleven subjects who were previously diagnosed with asthma, EIB was present in all subjects (100%) in the compact EVH group, while it was presented in ten subjects (90.91%) in the conventional EVH challenge group. Of the eleven healthy subjects, EIB was present in one subject (4.55%) in the compact EVH group, while it was not present in the conventional EVH group. Experimental results showed that the compact EVH system has potential to become an alternative tool for EIB detection.

Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography

STUDY OBJECTIVES

To validate a contact-free system designed to achieve maximal comfort during long-term sleep monitoring, together with high monitoring accuracy.

METHODS

We used a contact-free monitoring system (EarlySense, Ltd., Israel), comprising an under-the-mattress piezoelectric sensor and a smartphone application, to collect vital signs and analyze sleep. Heart rate (HR), respiratory rate (RR), body movement, and calculated sleep-related parameters from the EarlySense (ES) sensor were compared to data simultaneously generated by the gold standard, polysomnography (PSG). Subjects in the sleep laboratory underwent overnight technician-attended full PSG, whereas subjects at home were recorded for 1 to 3 nights with portable partial PSG devices. Data were compared epoch by epoch.

RESULTS

A total of 63 subjects (85 nights) were recorded under a variety of sleep conditions. Compared to PSG, the contact-free system showed similar values for average total sleep time (TST), % wake, % rapid eye movement, and % non-rapid eye movement sleep, with 96.1% and 93.3% accuracy of continuous measurement of HR and RR, respectively. We found a linear correlation between TST measured by the sensor and TST determined by PSG, with a coefficient of 0.98 (R = 0.87). Epoch-by-epoch comparison with PSG in the sleep laboratory setting revealed that the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively.

CONCLUSIONS

TST estimates with the contact-free sleep monitoring system were closely correlated with the gold-standard reference. This system shows good sleep staging capability with improved performance over accelerometer-based apps, and collects additional physiological information on heart rate and respiratory rate.

Robust remote monitoring of breathing function by using infrared thermography

An abnormal breathing rate (BR) is one of the strongest markers of physiological distress. Moreover, it plays an important role in early detection of sudden infant death syndrome, as well as in the diagnosis of respiratory disorders. However, the current measuring modalities can cause discomfort to the patient, since attachment to the patient's body is required. This paper proposes a new approach based on infrared thermography to remotely monitor BR. This method allows to (1) detect automatically the nose, (2) track the associate region of interest (ROI), and (3) extract BR. To evaluate the performance of this method, thermal recording of 5 healthy subjects were acquired. Results were compared with BR obtained by capnography. The introduced approach demonstrated an excellent performance. ROIs were precisely segmented and tracked. Furthermore, a Bland-Altman diagram showed a good agreement between estimated BR and gold standard. The mean correlation and mean absolute BR error are 0.92 ± 0.07 and 0.53 bpm, respectively. In summary, infrared thermography seems to be a great, clinically relevant alternative to attached sensors, due to its outstanding characteristics and performance.

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human–machine interaction.

Evaluation of Ultrasound-Based Sensor to Monitor Respiratory and Nonrespiratory Movement and Timing in Infants

GOAL

To describe and validate a noncontacting sensor that used reflected ultrasound to separately monitor respiratory, nonrespiratory, and caretaker movements of infants.

METHODS

An in-phase and quadrature (I & Q) detection scheme provided adequate bandwidth, in conjunction with postdetection filtering, to separate the three types of movement. The respiratory output was validated by comparing it to the electrical activity of the diaphragm (Edi) obtained from an infant ventilator in 11 infants. The nonrespiratory movement output was compared to movement detected by miniature accelerometers attached to the wrists, ankles, and heads of seven additional infants. Caretaker movement was compared to visual observations annotated in the recordings.

RESULTS

The respiratory rate determined by the sensor was equivalent to that from the Edi signal. The sensor could detect the onset of inspiration significantly earlier than the Edi signal (23+/-69 ms). Nonrespiratory movement was identified with an agreement of 0.9 with the accelerometers. It potentially interfered with the respiratory output an average of 4.7+/-4.5% and 14.9+/15% of the time in infants not requiring or on ventilatory support, respectively. Caretaker movements were identified with 98% sensitivity and specificity. The sensor outputs were independent of body coverings or position.

CONCLUSION

This single, noncontacting sensor can independently quantify these three types of movement.

SIGNIFICANCE

It is feasible to use the sensor as trigger for synchronizing mechanical ventilators to spontaneous breathing, to quantify overall movement, to determine sleep state, to detect seizures, and to document the amount and effects of caretaker activity in infants.

Assessment of human respiration patterns via noncontact sensing using Doppler multi-radar system

Human respiratory patterns at chest and abdomen are associated with both physical and emotional states. Accurate measurement of the respiratory patterns provides an approach to assess and analyze the physical and emotional states of the subject persons. Not many research efforts have been made to wirelessly assess different respiration patterns, largely due to the inaccuracy of the conventional continuous-wave radar sensor to track the original signal pattern of slow respiratory movements. This paper presents the accurate assessment of different respiratory patterns based on noncontact Doppler radar sensing. This paper evaluates the feasibility of accurately monitoring different human respiration patterns via noncontact radar sensing. A 2.4 GHz DC coupled multi-radar system was used for accurate measurement of the complete respiration patterns without any signal distortion. Experiments were carried out in the lab environment to measure the different respiration patterns when the subject person performed natural breathing, chest breathing and diaphragmatic breathing. The experimental results showed that accurate assessment of different respiration patterns is feasible using the proposed noncontact radar sensing technique.