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Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

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Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

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Abstract

Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This paper provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography (ECG), seismo- and ballistocardiography (SCG/BCG), reflective photoplethysmography (PPG) and PPG imaging (PPGI), thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these sub-fields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.

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... Heart rate (HR), oxygenation levels (SPO 2 ), body temperature (BT), and blood pressure (BP) are critical physiological signals that reflect human health [1]. For example, in cardiovascular disease, a decrease in HR may reflect a rise in intracranial pressure [2], whilst an increase in HR may reflect hypovolaemic shock [3]. ...
... Blood oxygen levels are currently assessed either by simple methods such as a finger probe placed around the tip of the finger or by more invasive procedures such as blood tests, typically, a needle into an artery in the wrist [10]. The former technique although simple is open to significant error and fluctuation, whilst the latter is painful Zheng et al. [26] (slight head movement) 40 5.95 7.03 0.85 1 Previous systems listed acquired data using lab-based studies (fixed environment) and did not acquire SPO 2 . ...
... Normalise Signal: [63] Signal channeltype = Signal channeltype np.linalg.norm(Signal channeltype ) (1) where channel type is R, G, B, Gy and IR. Interpolation: The signal obtained is interpolated by estimating the linespace using length of the signal data and its maximum time maximumTime = Len_S/FPS_channel and then apply interpolation as mentioned below: nterpolatedData = np.interp(eventimes, ...
Article
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Current methods of measuring heart rate (HR) and oxygen levels (SPO2) require physical contact, are individualised, and for accurate oxygen levels may also require a blood test. No-touch or non-invasive technologies are not currently commercially available for use in healthcare settings. To date, there has been no assessment of a system that measures HR and SPO2 using commercial off-the-shelf camera technology that utilises R, G, B, and IR data. Moreover, no formal remote photoplethysmography studies have been performed in real-life scenarios with participants at home with different demographic characteristics. This novel study addresses all these objectives by developing, optimising, and evaluating a system that measures the HR and SPO2 of 40 participants. HR and SPO2 are determined by measuring the frequencies from different wavelength band regions using FFT and radiometric measurements after pre-processing face regions of interest (forehead, lips, and cheeks) from colour, IR, and depth data. Detrending, interpolating, hamming, and normalising the signal with FastICA produced the lowest RMSE of 7.8 for HR with the r-correlation value of 0.85 and RMSE 2.3 for SPO2. This novel system could be used in several critical care settings, including in care homes and in hospitals and prompt clinical intervention as required.
... In addition, such system could be used in a broader context as part of a home health monitoring system, further expanding coverage and connectivity. Four primary vital signs are nowadays used for bedside monitoring and assessment of patient´s status: heart rate (HR), respiratory rate (RR), blood pressure (BP) and body temperature (BP) [19][20][21][22][23][24]. These methods usually require close physical interaction with the examined patient (cables, electrodes, cuffs, etc.), which makes them suitable for clinical environments but limits their deployment to non-hospital applications [19,22]. ...
... The development of technology for unobtrusive and non-contact vital sign monitoring has brought a large amount of attention in recent years [22][23][24]27]. Active driver status monitoring has been a major research topic for a long time, as it has a significant impact on road safety and accident statistics. ...
... Bruser et al. [23] explained in their work the application of nine different non-contact sensing modalities, which measured cardiorespiratory activity by sensing mechanical, bioelectric and thermal effects caused by various body disorders-specifically cECG, SCG, BCG, pulse oximetry, thermography, laser, radar methods, video motion analysis, as well as methods using high-frequency electromagnetic fields. When pressure or acceleration sensors are attached to the chest for the purpose of heart movements' recording, it results, in particular, in BCG signal measurement [58][59][60]. ...
Article
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This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.
... This method usually requires direct contact between electrodes and skin. Conversely, the feasibility of measuring to measure signals through clothes in a noncontact way has also been investigated: the gathering of heart-related information has proven more challenging, specifically due to interference from stronger noises compared to those from the chest impedance change [103,104]. The second method, the magnetic impedance method uses: a magnetic field generated from coils close to the chest to measure the induced eddy current, which is regulated by chest impedance and thus contains cardiorespiratory information [105]. ...
... Additionally, the ECG and PPG signals measured by cECG or rPPG techniques contain the most salient physiological information because they are the most similar to the clinical cardiac signals. A comparison of a variety of characteristics of different techniques can be found in Bruser et al. [104]. ...
Article
With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients’ health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.
... On the other hand, acquisition of physiological variables through non-contact methods have also been a recurring topic in the past few years. Nowadays, signals such as respiration or heart rate can be easily acquired with a wide variety of methods [11][12][13], comprising from ultrasound systems [11] to more advanced Doppler radar-based methods [12]. ...
... In order to extract the respiratory signal from the pattern, the pattern must be placed on the chest of the subject. Then, the variations on the computed distance are proportional to the displacement of the thorax [13], hence proportional to the respiration of the subject. The concatenation of this computed distance for each frame conforms the Raw Respiratory Signal as it can be seen in Fig. 3. ...
Article
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The aim of this work is to present a non-contact video-based method for respiratory rhythm extraction. The method makes use of a consumer-grade RGB camera, and it is based on computer vision algorithms to detect and track a custom pattern placed on the thorax of the subject. The respiratory signal is extracted by computing the changes in the position of the detected pattern through time. The method has been validated by comparing the extracted respiratory signal versus the one obtained with a reference method in adult population. The reference method was an inductive thorax plethysmography system (Respiband system from BioSignalsPlux™). 21 healthy subjects were measured and four tests were performed for each subject. The respiratory signals and its respiratory cycles were extracted. To characterise the error, the respiratory cycles were assessed with: the Fisher intra-class correlation (ICC), mean absolute error (MAE), the mean absolute percentage error (MAPE) and four Bland-Altman plots were obtained. The results show a >0.9 correlation for controlled respiration and >0.85 for unconstrained respiration between the proposed method and the reference method, with low error results (MAPE <4% for constrained respiration and <6% for unconstrained respiration) and with a high sensitivity when detecting the respiratory cycles (>94% in all cases). From the obtained results we can conclude that the proposed algorithm is adequate to acquire the respiratory signal for rhythm extraction, in real-time with a high performance when compared with the reference method, and that it could be applied to real-life situations.
... Fig. 1a shows various components of ECG signal which are of two types, viz., morphological features: P-wave, QRS-complex, Twave, and U-wave and interval features: PR-segment, ST-segment, PR interval, ST interval, RR interval, and so on [3][4][5]. ECG signals have a wide variety of applications in the medical domain such as cardiorespiratory monitoring, seizure detection and monitoring, ECG-based biometrics authentication, real-time analysis of electrocardiographic rhythm, heart-rate variability analysis using smart electrocardiography patch, and study of cardiac ischemia [6][7][8][9][10][11]. These applications require a proper determination of the morphological and interval aspects of the recorded ECG signal, which are susceptible to various kinds of predominant noises such as base-line wander (BW), muscle artefacts (MA) or electromyogram (EMG) noise, channel noise (additive white Gaussian noise, AWGN), power-line interference (PLI), and miscellaneous noises such as composite noise (CN), random noise, electrode motion artefacts (EM), and instrumentation noise, making it challenging to determine disease-specific morphological anomalies in the ECG signals. ...
... where L 2 ℝ is the space of square-integrable functions in ℝ. It follows from [69] if a function is square-integrable, then it can be expressed as follows: 6 Schematic diagram of DAE [19], where x: normalised input, x: corrupted input, z: partial information of input, and x: reconstructed output where ...
Article
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An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre-processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state-of-the-art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root-mean-square error, percentage-root-mean-square difference, and signal-to-noise ratio improvement, thus comparing various ECG denoising techniques on MIT-BIH databases, PTB, QT, and other databases. It is observed that Wavelet-VBE, EMD-MAF, GAN2, GSSSA, new MP-EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP-EKF, DLSR, and AKF perform comparatively well. For base-line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power-line interference removal, DLSR and EWT perform well. Finally, FCN-based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.
... In these cases, contactless vital sign monitoring methods provide suitable alternatives. [4,5] Camera-based photoplethysmography (cbPPG), also called imaging or remote photoplethysmography, is a non-contact optical measurement technique that uses cameras to assess cardiovascular vital signs such as heart rate (HR). Blood volume pulsation in the vessels and ballistocardiographic effects lead to pulsatile alterations of optical parameters in the skin tissue and therefore modulate the number of reflected photons reaching the camera. ...
... Towards the outside, the relative weighting of the RGB channels became more differentiated. Regarding the illustration of CHROM and POS in Fig. 6, a fundamental difference between those algorithms and the grid search color combinations must be noted: While the grid search color combinations (see (4)) remained static during the whole signal, CHROM and POS (see Table 1) update their tuning parameter every 1.6 s, which leads to dynamic color combinations. For this reason, the CHROM and POS references in Fig. 6 can only serve as a rough orientation. ...
Article
Objective: The heart rate is an essential vital sign that can be measured remotely with camera-based photoplethysmography (cbPPG). Systems for cbPPG typically use cameras that deliver red, green, and blue (RGB) channels. The combination of these channels has been proven to increase signal-to-noise ratio (SNR) and heart rate measurement accuracy (ACC). However, many combinations remain untested, the comparison of proposed combinations on large datasets is insufficiently investigated, and the interplay with skin tone is rarely addressed. Methods: Eight regions of interest and eight color spaces with a total of 25 color channels were compared in terms of ACC and SNR based on the Binghamton-Pittsburgh-RPI Multimodal Spontaneous Emotion Database (BP4D+). Additionally, two systematic grid searches were performed to evaluate ACC in the space of linear combinations of the RGB channels. Results: Glabella and forehead regions of interest provided highest ACC (up to 74.1 %) and SNR (> -3 dB) with the hue channel H from HSV color space and the chrominance channel Q from NTSC color space. The grid searches revealed a global optimum of linear RGB combinations (ACC: 79.2 %). This optimum occurred for all skin tones, although ACC dropped for darker skin tones. Conclusion: Through systematic grid searches we were able to identify the skin tone independent optimal linear RGB color combination for measuring heart rate with cbPPG. Our results proved on a large dataset that the identified optimum outperformed conventionally used color channels. Significance: The presented findings provide useful evidence for future considerations of algorithmic approaches for cbPPG.
... When it comes to early diagnosing and preventing potential human coronary system problems, long-term monitoring has been drastically gaining popularity within non-clinical environments in recent years. Non-invasive and unobtrusive sensing techniques present simple and affordable solutions to healthcare professionals in providing preclinical data [2]. ...
... Seismocardiogram and ballistocardiogram can easily be recorded by any smartphone accelerometer sensor in daily life today [7]. However, these signals are exceedingly sensitive to peripheral distortions such as body movements, cardiac and respiratory sounds, even an improper posture of the body and this is currently the biggest challenge which should essentially be overcome [2], [5], [8]. No sufficient number of studies have been fulfilled to give a clinical significance to cardiomechanical signals yet, so SCG and BCG still could not find any use for diagnostic purposes alone. ...
Article
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Seismocardiogram (SCG) is a low-cost monitoring method to collect precordial vibrations of sternum due to heartbeats and evaluate cardiac activity. It is mostly used as an auxiliary measurement to the other monitoring methods; however, it carries significant patterns reflecting current cardiovascular health status of subjects. If it is properly collected within a non-clinical environment, it might be able to present preliminary data to physicians before clinic. SCG signals are morphologically noisy. These signals store excessive amount of data. Extracting significant information corresponding to heartbeat complexes is so important. Previously, the method called compressed sensing (CS) had been applied to weed up the redundant information by taking the advantage of sparsity feature in a study. This compressed sensing is based on storing significant signals below the Nyquist rate which suffice for medical diagnosis. It has been feasible to compress SCG signals with 3:1 compression rate at least while maintaining accurate signal reconstruction. Nevertheless, higher compression rates lead to the formation of artifacts on reconstructed signals. This limits a more aggressive compression to reduce the amount of data. The requirement of a different approach which will allow higher compression rates and lower loss of information arises. The purpose of this study is to obtain more competent results by using a method called predefined signature and envelope vector sets (PSEVS) which has been satisfyingly applied to electrocardiogram (ECG) and speech signals. In the study, simultaneously recorded ECG and SCG signals were modeled with the method called PSEVS. The reconstructed signals were compared to the original signals so as to investigate the efficacy of signature-based modeling methods in constructing medically remarkable biosignals for clinical use. After examining the components of reconstructed signals called frame-scaling coefficient, signature and envelope vectors, it has been seen that the error function values of envelope vectors differ from expected values. We concluded that reconstructed SCG signals were not adequate for medical diagnosis.
... ECG, EEG, and EMG are action potential, whereas other signals such as speech signals, phonocardiogram (PCG), catheter-tip sensor signals, and vibroarthrogram (VAG) are categorised as event-related potentials [ 2 ]. From the above-mentioned applications, ECG signals were mostly used for diagnosing many applications such as cardiovascular problems, sleep apnoea, emotional, and arrhythmias detection [ 7,8,9,10 ]. ECG signal comprises of PQRST wave form which can be subdivided as P-wave, QRS complex, T-wave, and U-wave [ 11,12 ]. ...
Conference Paper
Human bodies constantly generate the signal or information which has valuable information about the health. These signals should be extracted from the body, and it has to be processed to diagnose the disease. Using these informations, the blood pressure, haemoglobin levels in blood, brain activities, heart functions, and etc. can be measured invasive and non-invasively. The signal obtained from the human body will contain noises, and it has low-amplitude raw data which cannot be used directly for the diagnose purpose. In order to make it to be useful, the signal should be filtered to remove the noise and amplified to extract the exact information. Signal processing contributes a major role in the medical technologies to diagnose the disease and provide solution to cure. In this review, applications of signal processing particularly in ECG medical technologies are discussed. The methods and algorithms to overcome the ECG noises are discussed briefly. Also, the improvement of those technologies for the betterment of human health care is also discussed.
... In fact, techniques for noncontact vital sign monitoring have recently gained a lot of attention for various applications. 1,2 Capacitive coupling, for example, to replace the cable-bound electrocardiogram (ECG) on the neonatal intensive care unit (NICU) has been investigated. 3 Recent reviews focus particularly on neonatal heart rate monitoring. ...
Article
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Magnetic induction measurement (MIM) is a noninvasive method for the contactless registration of respiration in newborn piglets by using measurement coils positioned at the bottom of an incubator. Acute pulmonary problems may be determinants of poor neurological and psychomotor outcomes in preterm infants. The current study tested the detection of pulmonary ventilation disorders via MIM in 11 newborn piglets. Six measurement coils determined changes in magnetic induction, depending on the ventilation of the lung, in comparison with flow resistance. Contactless registration of induced acute pulmonary ventilation disorders (apnea, atelectasis, pneumothorax, and aspiration) was detected by MIM. All pathologies except aspiration were detected by MIM. Significant changes occurred after induction of apnea (three coils), malposition of the tube (one coil), and pneumothorax (three coils) (p ≤ 0.05). No significant changes occurred after induction of aspiration (p = 0.12). MIM seems to have some potential to detect acute ventilation disorders in newborn piglets. The location of the measurement coil related to the animal’s position plays a critical role in this process. In addition to an early detection of acute pulmonary problems, potential information pointing to a therapeutic intervention, for example, inhalations or medical respiratory analepsis, may be conceivable with MIM in the future. MIM seems to be a method in which noncontact ventilation disorders of premature and mature infants can be detected. This study is an extension of the experimental setup to obtain preliminary evidence for detection of respiratory activity in neonatal piglets. For the first time, MIM is used to register acute ventilation problems of neonates. The possibility of an early detection of acute ventilation problems via MIM may provide an opportunity to receive patient-side information for therapeutical interventions like inhalations or medical respiratory analepsis.
... In addition to ECG, the photoplethysmogram (PPG) signal has become an indispensable tool for detecting and diagnosing the blood oxygen saturation (SpO2), blood glucose, blood pressure, sleep apnea, etc., [47]. To speed-up and improve the quality of diagnosis, a few attempts have been made to acquire, monitor, and process multiple bio-signals (MBioSigs) such as ECG, PPG, ballistocardiogram (BCG), electromyogram (EMG) signals, respiration and body temperature [47,49,[50][51][52][53][54][55]. Shared goals of all these developments are to produce a comprehensible snapshot of the patient and also to make it easier for the healthcare professionals to reach a conclusion. ...
... Previous studies have shown that noncontact cardiac monitoring techniques can be used by subjects in daily life for long-term cardiac activity monitoring [18]. The ballistocardiogram (BCG) [19] and seismocardiogram (SCG) [20] capture the body's mechanical responses to cardiac activity and blood circulation. ...
Article
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Atrial fibrillation (AF) is the most common arrhythmia and can seriously threaten patient health. Research on AF detection carries important clinical significance. This manuscript proposes an AF detection method based on ballistocardiogram (BCG) signals collected by a noncontact sensor. We first constructed a BCG signal dataset consisting of 28,214 ten-second nonoverlapping segments collected from 45 inpatients during overnight sleep, including 9438 for AF, 9570 for sinus rhythm (SR), and 9206 for motion artifacts (MA). Then, we designed a residual convolutional neural network (CNN) for AF detection. The network has four modules, namely a downsampling convolutional module, a local feature learning module, a global feature learning module, and a classification module, and it extracts local and global features from BCG signals for AF detection. The model achieved precision, sensitivity, specificity, F1 score, and accuracy of 96.8%, 93.7%, 98.4%, 95.2%, and 96.8%, respectively. The results indicate that the AF detection method proposed in this manuscript could serve as a basis for long-term screening of AF at home based on BCG signal acquisition.
... Unobtrusive measurement of vital signs has been a topic of high interest in recent research [1]. The use of capacitively-coupled ECG (ccECG), for instance, has been proposed as a solution that allows long-term monitoring and can achieve results similar to medical grade ECG [2]. ...
... AR modelling and pole cancellation utilized in [19] cancels out aliased frequency components caused by artificial light flicker and constucts accurate HR spectrum from AR model. The rPPG technology is widely concerned and applied in the fields of medical imaging research [13], emotion recognition [14] and face antispoofing [15]. Lately, experimental verification in [16] shows that spatial coherence and temporal consistency of biological signals can be used to achieve Deepfake detection, with the video accuracy up to 94.65%, which is the first time HR signals is mentioned in the field of deep forgery. ...
Preprint
Deepfake poses a serious threat to the reliability of judicial evidence and intellectual property protection. In spite of an urgent need for Deepfake identification, existing pixel-level detection methods are increasingly unable to resist the growing realism of fake videos and lack generalization. In this paper, we propose a scheme to expose Deepfake through faint signals hidden in face videos. This scheme extracts two types of minute information hidden between face pixels-photoplethysmography (PPG) features and auto-regressive (AR) features, which are used as the basis for forensics in the temporal and spatial domains, respectively. According to the principle of PPG, tracking the absorption of light by blood cells allows remote estimation of the temporal domains heart rate (HR) of face video, and irregular HR fluctuations can be seen as traces of tampering. On the other hand, AR coefficients are able to reflect the inter-pixel correlation, and can also reflect the traces of smoothing caused by up-sampling in the process of generating fake faces. Furthermore, the scheme combines asymmetric convolution block (ACBlock)-based improved densely connected networks (DenseNets) to achieve face video authenticity forensics. Its asymmetric convolutional structure enhances the robustness of network to the input feature image upside-down and left-right flipping, so that the sequence of feature stitching does not affect detection results. Simulation results show that our proposed scheme provides more accurate authenticity detection results on multiple deep forgery datasets and has better generalization compared to the benchmark strategy.
... PPG and pulse oximetry (an application of dual wavelength PPG) are extensively used to measure hemodynamic parameters such as heart rate, respiration rate, blood oxygen saturation, and even blood pressure [2,3]. PPG technology can be found in a variety of recent commercial products and form factors including finger clip sensors, smartwatches, smartphones, and fitness trackers, as it provides valuable health indicators in a continuous, convenient, and noninvasive way [4]. ...
Article
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Photoplethysmography is an extensively-used, portable, and noninvasive technique for measuring vital parameters such as heart rate, respiration rate, and blood pressure. The deployment of this technology in veterinary medicine has been hindered by the challenges in effective transmission of light presented by the thick layer of skin and fur of the animal. We propose an injectable capsule system to circumvent these limitations by accessing the subcutaneous tissue to enable reliable signal acquisition even with lower light brightness. In addition to the reduction of power usage, the injection of the capsule offers a less invasive alternative to surgical implantation. Our current prototype combines two application-specific integrated circuits (ASICs) with a microcontroller and interfaces with a commercial light emitting diode (LED) and photodetector pair. These ASICs implement a signal-conditioning analog front end circuit and a frequency-shift keying (FSK) transmitter respectively. The small footprint of the ASICs is the key in the integration of the complete system inside a 40-mm long glass tube with an inner diameter of 4 mm, which enables its injection using a custom syringe similar to the ones used with microchip implants for animal identification. The recorded data is transferred wirelessly to a computer for post-processing by means of the integrated FSK transmitter and a software-defined radio. Our optimized LED duty cycle of 0.4% at a sampling rate of 200 Hz minimizes the contribution of the LED driver (only 0.8 mW including the front-end circuitry) to the total power consumption of the system. This will allow longer recording periods between the charging cycles of the batteries, which is critical given the very limited space inside the capsule. In this work, we demonstrate the wireless operation of the injectable system with a human subject holding the sensor between the fingers and the in vivo functionality of the subcutaneous sensing on a pilot study performed on anesthetized rat subjects.
... As such, Leonhardt et al. [203] suggest the simultaneous use of different respiratory sensors and the development of sensor fusion algorithms to provide a more robust measure of f R . Optical sensors, radiofrequency sensors, and strain/pressure sensors embedded in instrumented chairs are also suitable for monitoring computer workers [23,[205][206][207][208]. Breath-by-breath f R estimated from video recordings is generally more accurate compared to other contactless techniques, with errors below 4 breaths/min in the 10-40 breaths/min f R range [206]. ...
Article
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Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise‐induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.
... The existing reviews on vital sign measuring systems by Scalise et al. [19] and Kranjec et al. [15] mainly provided a general overview of both the contact-based and non-contact methods for the measurement of vital signs. Brueser et al. [20] presented a study of suitable techniques according to the physiological effect caused by heart or lung activity. The review mainly focused on different attributes offered by body organs while performing the heartbeat and respiration activity. ...
Article
Vital signs are inarguably accepted as important key constituents to improve the health condition. Worldwide medical institutions and clinical observations have emphasized the need for continuous monitoring of vital signs such as heart rate (HR) and respiration rate (RR) for better health management. Radars are investigated as one of the potential technologies for non-contact continuous monitoring of vital signs. This paper provides a comprehensive technological review on the current state-of-art of non-contact vital sign (NCVS) measurements using radar. We highlight the need to move towards higher frequency for high accuracy in a multi-resident environment and analyze the implications of mmWave exposure on human health and the effect of environmental attenuation. Significant challenges associated with hardware and signal processing algorithm are discussed in detail. Finally, we conclude the review with future directions and challenges associated with the detection of vital signs in a multi-resident indoor environment.
... Although transmission mode PPG sensing is widely used in clinical settings for pulse oximetry measurements, reflectance mode PPG and PPG sensing for other physiological measurements has not been widely adopted in clinical practice. One of the main factors affecting PPG sensing performance is its susceptibility to interference, predominantly from motion artifacts [16]. Other significant factors affecting the performance include the amount of blood flowing into the peripheral vascular bed, the varying optical properties of skin and blood, ambient light, and the wavelength used to illuminate the skin [5]. ...
Article
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Optical pulse detection photoplethysmography (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics.
... 14 Applications include floor tiles for fall detection, 15 mirrors reflecting the health status, 16 or unobtrusive vital sign measurement. 17,18 Today, vehicles already host more than 100 sensors for external and internal climate (e.g., humidity, light, and temperature), vehicle-(e.g., wheel pressure and window opening), engine-(e.g., water and oil temperatures), trip-(e.g., speed, acceleration, and front radar), and occupantsrelated (e.g., seat occupation and camera) information as well as for feeding active driving assistance. 19 Current research yields further sensor types, for instance, adaptive airbags 20 and sensors monitoring the health status of passengers. ...
Article
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Background The rapid dissemination of smart devices within the internet of things (IoT) is developing toward automatic emergency alerts which are transmitted from machine to machine without human interaction. However, apart from individual projects concentrating on single types of accidents, there is no general methodology of connecting the standalone information and communication technology (ICT) systems involved in an accident: systems for alerting (e.g., smart home/car/wearable), systems in the responding stage (e.g., ambulance), and in the curing stage (e.g., hospital). Objectives We define the International Standard Accident Number (ISAN) as a unique token for interconnecting these ICT systems and to provide embedded data describing the circumstances of an accident (time, position, and identifier of the alerting system). Materials and Methods Based on the characteristics of processes and ICT systems in emergency care, we derive technological, syntactic, and semantic requirements for the ISAN, and we analyze existing standards to be incorporated in the ISAN specification. Results We choose a set of formats for describing the embedded data and give rules for their combination to generate an ISAN. It is a compact alphanumeric representation that is generated easily by the alerting system. We demonstrate generation, conversion, analysis, and visualization via representational state transfer (REST) services. Although ISAN targets machine-to-machine communication, we give examples of graphical user interfaces. Conclusion Created either locally by the alerting IoT system or remotely using our RESTful service, the ISAN is a simple and flexible token that enables technological, syntactic, and semantic interoperability between all ICT systems in emergency care.
... Health monitoring parameters (such as heart and respiration rate) measured by noninvasive sensing methods have been the object of study over the past decades [1]. In recent years, portable personal monitoring systems or wearable systems have been developed for monitoring the current state of a patient's health in everyday situations [2]. ...
Article
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Heart rate (HR) is an essential indicator of health in the human body. It measures the number of times per minute that the heart contracts or beats. An irregular heartbeat can signify a severe health condition, so monitoring heart rate periodically can help prevent heart complications. This paper presents a novel wearable sensing approach for remote HR measurement by a compact resistance-to-microcontroller interface circuit. A heartbeat’s signal can be detected by a Force Sensing Resistor (FSR) attached to the body near large arteries (such as the carotid or radial), which expand their area each time the heart expels blood to the body. Depending on how the sensor interfaces with the subject, the FSR changes its electrical resistance every time a pulse is detected. By placing the FSR in a direct interface circuit, those resistance variations can be measured directly by a microcontroller without using either analog processing stages or an analog-to-digital converter. In this kind of interface, the self-heating of the sensor is avoided, since the FSR does not require any voltage or bias current. The proposed system has a sampling rate of 50 Sa/s, and an effective resolution of 10 bits (200 mΩ), enough for obtaining well-shaped cardiac signals and heart rate estimations in real time by the microcontroller. With this approach, the implementation of wearable systems in health monitoring applications is more feasible.
... ECG, EEG, and EMG are action potential, whereas other signals such as speech signals, phonocardiogram (PCG), catheter-tip sensor signals, and vibroarthrogram (VAG) are categorised as event-related potentials [ 2 ]. From the above-mentioned applications, ECG signals were mostly used for diagnosing many applications such as cardiovascular problems, sleep apnoea, emotional, and arrhythmias detection [ 7,8,9,10 ]. ECG signal comprises of PQRST wave form which can be subdivided as P-wave, QRS complex, T-wave, and U-wave [ 11,12 ]. ...
... In such wearable devices, continuous monitoring of heart rate (HR) and heart rate variability (HRV) is a key feature that provides critical implications about the user's cardiovascular health status [98]. Among the various methods to assess HR and HRV, photoplethysmography (PPG) is the popular choice of measurement in compact wearable devices because of its unobtrusive nature [99]. ...
Thesis
The relentless pursuit of financial efficiency has encouraged the development of intensive animal management systems, where the care of the animal is sometimes compromised. As the physical or emotional stress on the animals summons the conscience of the consumers, the public's interest in animal welfare is continuing to rise. While several qualitative and quantitative measures are used to assess the long-term welfare of an animal, the physiological and behavioral states of the animals are the only quantifiable measures of the short-term responses of animal welfare. Moreover, studying the vital signs [e.g., heart rate (HR), breathing rate (BR), blood pressure (BP), core body temperature, etc.] and behavioral traits of freely moving animals can provide significant insights to veterinarians, animal researchers, and biomedical engineers. Monitoring of animals is also necessary for the pharmaceutical industries, where the safety and efficacy of human drugs are tested on animal models. Wireless sensor systems attached to individual animals can provide specific physio-behavioral information about each animal continuously. However, an externally attached device on a freely moving animal would have unfavorable impacts on its natural behavior and comfort. Moreover, the recordings from a wearable sensor would suffer from the obstruction created by the layer of skin and fur. An implantable system, on the other hand, can avoid the difficulties related to the attachment of sensors to the animal and can be minimally obtrusive, depending on the size of the implant. In this research, a subcutaneously injectable implant equipped with several sensing capabilities is developed using commercial-off-the-shelf components. First, the transparently encapsulated implant includes a biophotonic front-end circuit that can acquire photoplethysmography (PPG) signals. The designed system successfully recorded PPG signals using light sources of different wavelengths from rats and chickens during \textit{in vivo} experiments. As PPG systems are highly power-consuming, a low-power custom-integrated PPG front-end circuit has been validated by developing a wearable wristband for humans that has the potential to reduce the implant’s battery usage in the future. Second, the developed system is capable of biopotential (electrocardiography or ECG) and bioimpedance (BIOZ) measurements that can provide deeper insight into the cardiovascular system. Despite the difficulties of interfacing conductive electrodes in implants, two techniques for manufacturing electrode surfaces on the implant are proposed, and the accuracy of the system is validated with a commercial ECG amplifier during the in-vivo experiments. The combination of this biophotonic and bioelectric sensing would enable the estimation of HR, BR, oxygen saturation in the blood (pulse oximetry), pulse transit time (PTT) which is correlated with BP, tissue hydration level, etc. Third, a temperature sensor has been added to read the core body temperature, which has been validated using an in-vitro setup. Lastly, an inertial measurement unit (IMU) that integrates an accelerometer and a magnetometer are included in the system. Accelerometry can track various micro and macro activities by classifying the tri-axial data, whereas magnetometry can register an animal's physical orientation. All these sensor electronics, along with a wireless microcontroller and a pin-type battery, are coated with biocompatible materials and packaged into a capsule-shaped cylinder with a diameter of 4 mm. This miniaturized implant fits into a commercially available injector (similar to the ones used for RFID tags) and allows for an easier injection method avoiding any surgical procedure on the animal. The contribution of this research includes the design and development of the implantable system, optimization of the hardware and software to reduce the power consumption, packaging innovations to accommodate electrical interfaces within the injectable form factor, and the in-vivo animal experiments for the validation of individual sensors.
... Contact-free monitoring of vital signs is an upcoming field enabling many new medical applications, but also providing increased comfort and ease-of-use for classical cable-bound environments such as the intensive care unit. Over the last few years there has been increasing interest in investigating and comparing all available technologies [46,47] including capacitive ECG, magnetic impedance, ballistocardiography, radar, and camera-based techniques operating in the visible light frequency range, the near-infrared frequency range, and the far infrared band (Infrared Thermography). As pointed out in the commentary by Marjonivic et al. [48], respiration rate is an often underutilized but nevertheless very important vital sign. ...
Article
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This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
... Additionally, NRR is an attractive biomarker because it can be measured by many different sensors. Adherenceindependent strategies can be based on strain gauges placed under the bed (20), mattress sensors (21)(22)(23)(24), or ultrawideband radar systems (25,26). For highly engaged patients, there are many wearables and smartphone apps capable of measuring respiratory rates (27)(28)(29). ...
Preprint
The days and weeks preceding hospitalization are poorly understood because they transpire before patients are seen in conventional clinical care settings. Home health sensors offer opportunities to learn signatures of impending hospitalizations and facilitate early interventions, however the relevant biomarkers are unknown. Nocturnal respiratory rate (NRR) is an activity-independent biomarker that can be measured by adherence-independent sensors in the home bed. Here, we report automated longitudinal monitoring of NRR dynamics in a cohort of high-risk recently hospitalized patients using non-contact mechanical sensors under patients home beds. Since the distribution of nocturnal respiratory rates in populations is not well defined, we first quantified it in 2,000 overnight sleep studies from the NHLBI Sleep Heart Health Study. This revealed that interpatient variability was significantly greater than intrapatient variability (NRR variances of 11.7 brpm2 and 5.2 brpm2 respectively, n=1,844,110 epochs), which motivated the use of patient-specific references when monitoring longitudinally. We then performed adherence-independent longitudinal monitoring in the home beds of 34 high-risk patients and collected raw waveforms (sampled at 80 Hz) and derived quantitative NRR statistics and dynamics across 3,403 patient-nights (n= 4,326,167 epochs). We observed 23 hospitalizations for diverse causes (a 30-day hospitalization rate of 20%). Hospitalized patients had significantly greater NRR deviations from baseline compared to those who were not hospitalized (NRR variances of 3.78 brpm2 and 0.84 brpm2 respectively, n= 2,920 nights). These deviations were concentrated prior to the clinical event, suggesting that NRR can identify impending hospitalizations. We analyzed alarm threshold tradeoffs and demonstrated that nominal values would detect 11 of the 23 clinical events while only alarming 2 times in non-hospitalized patients. Taken together, our data demonstrate that NRR dynamics change days to weeks in advance of hospitalizations, with longer prodromes associating with volume overload and heart failure, and shorter prodromes associating with acute infections (pneumonia, septic shock, and covid-19), inflammation (diverticulitis), and GI bleeding. In summary, adherence-independent longitudinal NRR monitoring has potential to facilitate early recognition and management of pre-symptomatic disease.
... Heart rate (HR) is an important physiological parameter for the assessment of physical health status, and is also a critical indicator for cardiovascular diseases [1][2][3][4][5][6]. In recent years, the imaging photoplethysmography (IPPG) method that could extract HR from the facial color variation information, has S392 C. Zhang et al. / Comparative study on the effect of color spaces and formats on IPPG-based HR measurement achieved rapid developments [7,8]. ...
Article
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Background: The imaging photoplethysmography (IPPG) technology has been demonstrated to be an effective method for heart rate (HR) monitoring. However, some interference caused by the ambient illumination variation and facial motion severely influences the accuracy of the HR measurement. Some color spaces and color formats are assumed to reduce the interference, and enhance the accuracy of HR estimation. Objective: The aim is to identify the optimal color space and format for IPPG based HR measurement. Methods: Six color spaces and 3 color formats are compared in this study, based on an IPPG based HR measurement system. 424 pieces of videos captured by the system are used for the selection of the optimal color channel and color space; while 10 pieces of videos are for the identification of the optimal color format. Results: The results shows that the green channel of RGB space is the optimal color channel, and RGB is the optimal color space, in respect of the mean squared error of HR estimation. BayerBG 8bit is found to be the optimal color format for video recording, which can significantly reduce the HR estimation error. Conclusions: BayerBG 8bit color format for video recording, and RGB color space for video analysis is suggested for the IPPG based HR measurement system. The suitable configuration of color space and format could enhance the accuracy of HR measurement.
... Heart rate is an important physiological parameter for the assessment of heart function and physical health status, and is also a clinical indicator for the treatment of cardiovascular diseases and some chronic diseases [1][2][3][4][5][6] . The contact photoplethysmography (PPG) is widely used for heart rate monitoring based on the optical absorption variations of the human skin due to the blood volume variations during the cardiac cycle [7][8][9] . ...
Article
The remote photoplethysmography technology based on consumer cameras has been demonstrated to be an effective method for heart rate monitoring. However, artificial signals caused by the ambient illumination variation and facial motion would severely distort the heart rate pulse signal and affect the measurement accuracy of the heart rate. In view of these issues, the conversion from RGB color space to LAB color space is performed to separate the luminance signal, and the smoothness prior approach is employed to remove the stationary artifacts in the raw signals of A channel and B channel. On this basis, the simple combined signal, the difference between A channel and B channel, is introduced to extract the heart rate pulse signal. Finally, the pure signal is decomposed to obtain a set of intrinsic mode functions using the ensemble empirical mode decomposition algorithm, and heart rate is estimated based on the one intrinsic mode function with the highest energy and peak ratio in the range of 0.7 Hz to 3 Hz. To assess the performance of the framework proposed in this paper, experiments in different scenarios are performed and the experimental results show that the method proposed in this paper can effectively estimate heart rate, where the mean absolute bias is 2.59 beats/min (bpm) and the 95% confidence interval is from -7.14 bpm to 3.40 bpm.
... Currently, doctors usually prefer non-contact devices and applications to examine their patients and manage healthcare. In this direction, remote-PPG (rPPG) plays a pivotal role for doctors and patients to remotely analyze the heart and respiration rates using the camera sensors [2] and thereby perform cardiac disease monitoring and chronic diseases treatment therapies [3,4]. Apart from telehealthcare, the rPPG is extensively explored in biometrics for liveness detection [5], spoof detection [6] and Deepfake detection [7]; and affective computing for microexpression spotting [8], micro-expression recognition [9] and stress analysis [10]. ...
Article
Heart rate (HR) estimation is an essential physiological parameter in the field of biomedical imaging. Remote Photoplethysmography (r-PPG) is a pathbreaking development in this field wherein the PPG signal is extracted from non-contact face videos. In the COVID-19 pandemic, rPPG plays a vital role for doctors and patients to perform telehealthcare. Existing rPPG methods provide incorrect HR estimation when face video contains facial deformations induced by facial expression. These methods process the entire face and utilize the same knowledge to mitigate different noises. It limits the performance of these methods because different facial expressions induce different noise characteristics depending on the facial region. Another limitation is that these methods neglect the facial expression for denoising even though it is the prominent noise source in temporal signals. These issues are mitigated in this paper by proposing a novel HR estimation method AND-rPPG, that is, A Novel Denoising-rPPG. We initiate the utilization of Action Units (AUs) for denoising temporal signals. Our denoising network models the temporal signals better than sequential architectures and mitigate the AUs-based (or face expression-based) noises effectively. The experiments performed on publicly available datasets reveal that our proposed method outperforms state-of-the-art HR estimation methods, and our denoising model can be easily integrated with existing methods to improve their HR estimation.
Article
Recent advances in the Medical Internet of Things (MIoT) and big data enable a prospering platform for pervasive healthcare and facilitate the transformation from hospital-centered to human-centered healthcare. Wearable devices as human interfaces provide first-hand data and real-time monitoring, which are key technologies in the MIoT. Several remarkable surveys have been conducted to summarize the recent progress in wearable sensors and systems for the MIoT and pervasive medicine. However, few have focused on wearable optical sensing (WOS) technologies, which is an emerging sensing modality in wearable devices. WOS can achieve high precision, high compatibility, high anti-interference, and low motion artifacts for human vital signal acquisition, which are particularly useful in special scenarios such as intensive care units (ICUs). These technologies can also be integrated with smart fabrics or mobile computing for out-of-hospital healthcare. This work provides the first literature review of WOS for pervasive medicine. We aim to systematically summarize the emerging WOS technologies in the MIoT for disease diagnosis and health monitoring. Specifically, this review covers the technical bases and design principles of major WOS technologies and their application domains for monitoring and treatment. We also discuss the opportunities and challenges, especially in the COVID-19 outbreak.
Article
In the wake of Big Data, traditional Machine Learn-ing techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data and transversal use of the same architectures in fundamentally different areas with weakly-substantiated adaptations. To address the former, a cardiorespiratory signal synthesizer was designed by conditional sampling from a multimodally trained stochastic system of Gaussian copulas integrated in a Markov chain. With respect to the latter, a multi-branch convolutional neural network architecture was conceived to learn the best cardiac sensor-fusion strategy at every abstraction layer. The network was tailored to the tasks of cycle detection and classification for different cardiac modality combinations by a synthesizer-based data augmentation training framework and Bayesian hyperparameter optimization. The synthesizer yielded highly realistic signals in the time, frequency and phase domains for both healthy and pathological heart cycles as well as artifacts of different modalities. Benchmarking suggested that the network is able to surpass previous architectures and data augmentation provided a performance boost in realistic data availability scenarios. These included insufficient training data volume, as low as 150 cycles long, artifact contamination and absence of a classification data type in training.
Conference Paper
Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.
Article
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A frequency-shift keying (FSK) radar in the 2.45-GHz band is proposed for highly accurate vital-signs detection. The measurement accuracy of the proposed detector for the heartbeat is increased by using the cross-correlation between the phase differences of signals at two frequencies used by the FSK radar, which alternately transmits and receives the signals with different frequencies. Two frequencies-2.45 and 2.5 GHz-are effectively discriminated by using the envelope detection with the frequency control signal of the signal generator in the output waveform of the FSK radar. The phase difference between transmitted and received signals at each frequency is determined after calibrating the I / Q imbalance and direct-current offset using a data-based imbalance compensation algorithm, the Gram-Schmidt procedure, and the Pratt method. The absolute-distance measurement results for a human being show that the vital signs obtained at each frequency using the proposed FSK radar have a cross-correlation. The heartbeat detection results for the proposed FSK radar at a distance of < 2.4 m indicate a reduction in the error rate and an increase in the signal-to-noise ratio compared with those obtained using a single operating frequency.
Article
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Wearables are valuable solutions for monitoring a variety of physiological parameters. Their application in cardiorespiratory monitoring may significantly impact global health problems and the economic burden related to cardiovascular and respiratory diseases. Here, we describe a soft biosensor capable of monitoring heart (HR) and respiratory (RR) rates simultaneously. We show that a skin-interfaced biosensor based on fiber optics (i.e., the smart patch) is capable of estimating HR and RR by detecting local ribcage strain caused by breathing and heart beating. The system addresses some of the main technical challenges that limit the wide-scale use of wearables, such as the simultaneous monitoring of HR and RR via single sensing modalities, their limited skin compliance, and low sensitivity. We demonstrate that the smart patch estimates HR and RR with high fidelity under different respiratory conditions and common daily body positions. We highlight the system potentiality of real-time cardiorespiratory monitoring in a broad range of home settings.
Article
Breathing rate (BR) is one of the vital signs used in physiological monitoring. Conventional BR monitoring requires attaching wired canula/thermistor on the buco-nasal area to measure air-flow, inducing discomfort to the subject. Abdominal/thoracic belts are also used to detect breathing movements whereas esophageal pressure is the gold standard to measure breathing effort. In this paper, we aim to validate the consistency of using only bed-sheet pressure sensors to monitor the BR in healthy adults. We propose a method and demonstrate that it could be used interchangeably with respiratory belts which were approved for medical use by the American Association of Sleep Medicine (AASM). We build a ten-sinusoidal model-based extended Kalman Filter to adaptively estimate the breathing movements’ signal from the body pressure distribution data. The model is posture-specific, I.e., parameters are optimized based on the detected posture. An artificial neural network (ANN) model was used to detect four bed postures to perform the kalman filter parameters’ optimization step. The BRs of 12 healthy adults are recorded using the pressure mattress and a reference respiratory belt. To validate the method as a surrogate measure, a Bland-Altman (BA) analysis was performed on both pressure and belt data, and the linear relationship is evaluated using Pearson Correlation Coefficient (PCC). Interestingly, a high inter-rater agreement, an average maximum difference of 1.93 Breaths Per Minute (BrPM), a confidence interval of 95%, along with a strong linear relationship of 95.8% on average between the two methods were interestingly obtained. The presented results show the suitability of the proposed solution in medical applications requiring respiration monitoring.
Article
Continuous monitoring of breathing rate (BR), minute ventilation (VE), and other respiratory parameters could transform care for and empower patients with chronic cardio-pulmonary conditions, such as asthma. However, the clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological signals, like ECG and motion, yet respiration tracking faces many challenges. In this work, we infer respiratory parameters from wearable ECG and wrist motion signals. We propose a modular and generalizable classification-regression pipeline to utilize available context information, such as physical activity, in learning context-conditioned inference models. Novel morphological and power domain features from the wearable ECG are extracted to use with these models. Exploratory feature selection methods are incorporated in this pipeline to discover application-driven interpretable biomarkers. Using data from 15 subjects, we evaluate two implementations of the proposed inference pipeline: for BR and VE. Each implementation compares generalized linear model, random forest, support vector machine, Gaussian process regression, and neighborhood component analysis as regression models. Permutation, regularization, and relevance determination methods are used to rank the ECG features to identify robust ECG biomarkers across models and activities. This work demonstrates the potential of wearable sensors not only in continuous monitoring, but also in designing biomarker-driven preventive measures.
Article
Purpose The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances. Design/methodology/approach COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO 2 ). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test. Findings IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO 2 ), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials. Originality/value The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t -test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).
Thesis
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Endüstri 4.0, hayatımızın her alanını şekillendiren teknolojileri ile sağlık sektörünün araştırma konusu olmuştur. Bu çalışma da Endüstri 4.0’ın sağlık sektöründe meydana getirdiği yeniliklerin (nesnelerin interneti, akıllı robotlar, giyilebilir teknolojiler, büyük veri, siber fiziksel sistemler ve yapay zeka) kullanım niyetinin altında yer alan temel faktörlerin tespiti amacıyla gerçekleştirilmiştir. Bu amaçla Davis (1986) ile Karahanna ve arkadaşları (2006) tarafından geliştirilen “Teknoloji Kabul Modeli”nden yararlanılmıştır. Araştırmada model içerisinde yer alan algılanan fayda, algılanan kullanım kolaylığı, tutum ve niyet boyutları incelenmiştir. Bu amaç doğrultusunda, Ankara ili Çankaya ilçesinde faaliyet gösteren 11 özel hastanede ve bir üniversite hastanesinde radyoloji bölümünde görev yapan toplamda 266 doktor, teknisyen ve hemşire ile anket çalışması yapılmıştır. Uygulama sonucunda elde edilen verilerin çözümlenmesinde Bağımsız Örneklem T-Testi, Basit Doğrusal Regresyon Analizi ve aracı değişken etkisinin değerlendirilmesinde ise Hayes’in (2013) SPSS (Sosyal Bilimler için İstatistik Paketi) için geliştirdiği Process Makro eklentisi kullanılmıştır. Process Makro’da, aracı değişken bootstrap model 4 analizi esas alınmıştır. Araştırma kapsamında oluşturulan hipotezler sonucunda, bireylerin Endüstri 4.0 teknolojilerini benimsemeye yönelik kullanım kolaylığı algısının, bu teknolojiyi kullanıma yönelik tutum ve fayda algısı üzerinde olumlu etkiye sahip olduğu görülmüştür. Yine, bireylerin Endüstri 4.0 teknolojilerini kullanmaya yönelik tutumlarının, bu teknolojiyi kabul niyeti üzerinde ve Endüstri 4.0 teknolojilerini benimsemeye yönelik fayda algılarının, bu teknolojiyi kullanımına yönelik tutumu üzerinde olumlu etkiye sahip olduğu ortaya çıkmıştır. Benzer şekilde fayda algısının, kullanım kolaylığı algısı ve tutum arasındaki ilişkide ve kullanım kolaylığı algısının, fayda algısı ve niyet arasındaki ilişkide kısmi aracı etkisinin olduğu belirlenmiştir. Son olarak görev yapılan kurum değişkenine göre ise anlamlı bir farklılık olmadığı sonucuna ulaşılmıştır.
Conference Paper
The increasing population size of the elderly is fostering the development of telehealth and assisted living systems. In this respect, monitoring vital biophysical conditions using wireless devices, such as the wireless electrocardiogram (WECG), plays a pivotal role in telemonitoring. However, the freedom of movement brings with it motion artifacts, the magnitude of which can be significant enough to interfere with the cardiac signals. To reason about and remove the artifacts, reference models (signals) are needed. In the context of WECGs, one way to construct these models is to employ motion sensors that can pick up the motion affecting the electrodes of the WECGs. In this paper, we experimentally examine the spectra of motion artifacts and the existence of correlations between inertial sensors and motion artifacts. We make use of three different types of sensors (3D accelerometer, 3D gyroscope, and skin-electrode impedance sensor) to assess the characteristics of different movement types. We found that the spectra of motion artifacts are determined by the type of movement. While lower-intensity motion artifacts (e.g., bending forward) are most pronounced below 2 Hz, others (e.g., running) manifest themselves in a series of distinct peaks between 1-10 Hz.Index Terms- accelerometer, electrocardiogram, gyroscope, inertial sensor, motion artifacts, skin-electrode impedance, tele-monitoring.
Article
In recent years, there has been an emergence of long-term cardiac monitoring devices, particularly as they relate to non-prescribed, user-initiated, wearable- and/or smartphone-based devices. With these new available data, practitioners are challenged to interpret these data in the context of routine clinical decision-making. While there are many potential uses for long-term rhythm monitoring, in this review, we will focus on the evolving role of this technology in atrial fibrillation (AF) monitoring after catheter and/or surgical ablation. Here, we explore the landscape of prescription-based tools for long-term rhythm monitoring; investigate commercially available technologies that are accessible directly to patients, and look towards the future with investigative technologies that could have a growing role in this space. This article is protected by copyright. All rights reserved.
Article
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Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.
Thesis
Endüstri 4.0, hayatımızın her alanını şekillendiren teknolojileri ile sağlık sektörünün araştırma konusu olmuştur. Bu çalışma da Endüstri 4.0’ın sağlık sektöründe meydana getirdiği yeniliklerin (nesnelerin interneti, akıllı robotlar, giyilebilir teknolojiler, büyük veri, siber fiziksel sistemler ve yapay zeka) kullanım niyetinin altında yer alan temel faktörlerin tespiti amacıyla gerçekleştirilmiştir. Bu amaçla Davis (1986) ile Karahanna ve arkadaşları (2006) tarafından geliştirilen “Teknoloji Kabul Modeli”nden yararlanılmıştır. Araştırmada model içerisinde yer alan algılanan fayda, algılanan kullanım kolaylığı, tutum ve niyet boyutları incelenmiştir. Bu amaç doğrultusunda, Ankara ili Çankaya ilçesinde faaliyet gösteren 11 özel hastanede ve bir üniversite hastanesinde radyoloji bölümünde görev yapan toplamda 266 doktor, teknisyen ve hemşire ile anket çalışması yapılmıştır. Uygulama sonucunda elde edilen verilerin çözümlenmesinde Bağımsız Örneklem T-Testi, Basit Doğrusal Regresyon Analizi ve aracı değişken etkisinin değerlendirilmesinde ise Hayes’in (2013) SPSS (Sosyal Bilimler için İstatistik Paketi) için geliştirdiği Process Makro eklentisi kullanılmıştır. Process Makro’da, aracı değişken bootstrap model 4 analizi esas alınmıştır. Araştırma kapsamında oluşturulan hipotezler sonucunda, bireylerin Endüstri 4.0 teknolojilerini benimsemeye yönelik kullanım kolaylığı algısının, bu teknolojiyi kullanıma yönelik tutum ve fayda algısı üzerinde olumlu etkiye sahip olduğu görülmüştür. Yine, bireylerin Endüstri 4.0 teknolojilerini kullanmaya yönelik tutumlarının, bu teknolojiyi kabul niyeti üzerinde ve Endüstri 4.0 teknolojilerini benimsemeye yönelik fayda algılarının, bu teknolojiyi kullanımına yönelik tutumu üzerinde olumlu etkiye sahip olduğu ortaya çıkmıştır. Benzer şekilde fayda algısının, kullanım kolaylığı algısı ve tutum arasındaki ilişkide ve kullanım kolaylığı algısının, fayda algısı ve niyet arasındaki ilişkide kısmi aracı etkisinin olduğu belirlenmiştir. Son olarak görev yapılan kurum değişkenine göre ise anlamlı bir farklılık olmadığı sonucuna ulaşılmıştır
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Normal oxygen transport (S A Clark). Motivation of pulse oximetry (D J Sebald). Blood oxygen measurement (J Farmer). Light absorbance in pulse oximetry (O Wieben). Light-emitting diodes and their control (B W J Bourgeois). Photodetectors and amplifiers (J S Schowalter). Probes (M V S Reddy). Electronic instrument control (K S Paranjape). Signal processing algorithms (S Palreddy). Calibration (J S Schowalter). Accuracy and errors (S Tungjitkusolmun). User interface for a pulse oximeter (A Lozano-Nieto). Applications of pulse oximetry (J B Ruchala). Glossary. Index.
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This paper presents the experimental setup and preliminary results of a CCD based Photoplethysmographic Imager (PPGI) which has been shown to be capable to assess various disorders of the peripheral venous system by standard test methods derived from the classical photophethysmographic practice in a noninvasive and non-contact way. The PPGI is a computer- based CCD imaging system to visualize the skin vessels and analyze the local changes of dermal blood volume. Our results show that this system performs as well as the currently available commercial PPG system by adding information of spatial distribution which allows the investigation of locations and causes of vascular disorders. Both the venous hemodynamics and arterial perfusion can be mapped in two dimensions.
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This paper presents the experimental setup and first results of a near infrared CCD based Photoplethysmographic Imager (PPGI) which has been shown to be suitable for contactless assessing various disorders of the peripheral venous system by standard test methods derived from the classical photoplethysmographic practice. The PPGI is a computer-based CCD near-infrared imaging system to visualize the skin vessels and analyze the local changes in dermal blood volume. Our current results show that this system performs as well as the available commercial PPG system by adding information of spatial distribution which allows the investigation of locations and causes of vascular disorders.
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This paper reports the development of an unconstrained sensing technique for monitoring the respiration and heartbeats during sleep using a polyvinylidene fluoride (PVDF) piezopolymer film sensor with the aim that the sensor can be used on the ordinary bed together with the condition that the use of the sensor does not interfere the daily sleep of the patient under measurement. A PVDF film is used as the sensory material in the sensor system. The film is placed under the sheet at the location of the thorax to pick up the fluctuation of the pressure on the bed caused by the respiratory movement and heartbeats. Wavelet multiresolution decomposition analysis is used for the detection of respiration and heartbeat from the sensor output. It is shown that the respiration and heartbeats can simultaneously be detected by the sensor with the use of the wavelet multiresolution decomposition analysis.
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For quantitative evaluation of body motion, a fully noncontact and unconstraining monitoring method was developed by introducing image sequence analysis. A spatiotemporal local optimization method was applied to determine optical flow in the image sequence. The optical flow visualized the apparent velocity field of the entire body motion, including both breast movement of respiration and posture changes in a bed. The experiment was carried out under regulated posture changes and under a sleeping condition by measuring heart rate, respiration and digitized image sequences using a video camera. A temporal increase in heart rate reflected the magnitude of physical activities. We proposed two candidate parameters for evaluation of respiratory and physical activities based on comparison among experimental results. The average of squared motion velocities reflected the magnitude of physical activities. The representative field-averaged component showed a waveform with periodic fluctuation corresponding to that of respiration obtained with a nasal thermistor.
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Prior to the successful use of non-contact photoplethysmography, several engineering issues regarding this monitoring technique must be considered. These issues include ambient light and motion artefacts, the wide dynamic signal range and the effect of direct light source coupling. The latter issue was investigated and preliminary results show that direct coupling can cause attenuation of the detected PPG signal. It is shown that a physical offset can be introduced between the light source and the detector in order to reduce this effect.
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In this article, the method of non-contact monitoring of cardiorespiratory activity by electromagnetic coupling with human tissue is investigated. Two measurement modalities were joined: an inductive coupling sensor based on magnetic eddy current induction and a capacitive coupling sensor based on displacement current induction. The systems sensitivity to electric tissue properties and its dependence on motion is analyzed theoretically as well as experimentally for the inductive and capacitive coupling path. The potential of both coupling methods to assess respiration and pulse without contact and a minimum of thoracic wall motion was verified by laboratory experiments. The demonstrator was embedded in a chair to enable recording from the back part of the thorax.
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Our work covers improvements in sensors and signal processing for unobtrusive, long-term monitoring of cardiac (and respiratory) rhythms using only non-invasive vibration sensors. We describe a system for the unobtrusive monitoring of vital signs by means of an array of novel optical ballistocardiography (BCG) sensors placed underneath a regular bed mattress. Furthermore, we analyze the systems spatial sensitivity and present proof-of-concept results comparing our system to a more conventional BCG system based on a single electromechanical-film (EMFi) sensor. Our preliminary results suggest that the proposed optical multi-channel system could have the potential to reduce beat-to-beat heart rate estimation errors, as well as enable the analysis of more complex breathing patterns.
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Ballistocardiography (BCG) is a non-invasive method for monitoring cardiovascular health. While in the early 1900s, when it was invented, the BCG was intended to be a tool used exclusively in the clinic, the recent resurgence of BCG research has actually focused on extra-clinical applications ranging from home monitoring to measuring signals from astronauts in space. This repositioning of the diagnostic technique has largely been spurred by recent advances in measurement technology: historically, BCG instrumentation was large, cumbersome, and difficult to maintain; currently, it is small, easy-to-use, and does not require any sophisticated maintenance. This review presents the latest technological improvements in BCG instrumentation. These developments should further help to establish the BCG as a useful diagnostic tool in the coming years.
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This work gives an overview about some non-contact methods for monitoring of physiological activity. In particular, the focus is on ballistocardiography, capacitive ECG, Infrared Thermography, Magnetic Impedance Monitroing and Photoplethymographic Imaging. The principles behind the methods are described and an inside into possible medical applications is offered.
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A skin motion imaging system with two modes of operation, diffusive and specular reflections, was developed. The system consists of image capturing and processing elements. Using optical flow analyses of skin motion at the wrist, we have detected successfully a blood pulsation signal that concurs with the electrocardiogram. The signal provides information not only about blood pulsation, but also about blood circulation and the biomechanical properties of the skin. This system may have other applications in the future, such as noncontact blood pulsation detection and evaluation of the biomechanical properties of skin, for example. © 1997 American Institute of Physics.
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There has been research interest in using radar for contact-less measurements of the human heartbeat for several years. While many systems have been demonstrated, not much attention have been given to the actual physical causes of why this work. The consensus seems to be that the radar senses small body movements correlated with heartbeats, but whether only the movements of the body surface or reflections from internal organs are also monitored have not been answered definitely. There has recently been proposed another theory that blood perfusion in the skin could be the main reason radars are able to detect heartbeats. In this paper an experimental approach is given to determine the physical causes. The measurement results show that it is the body surface reflections that dominate radar measurements of human heartbeats.
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We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.
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A system has been developed based on the measurement of skin surface vibration that can be used to detect the underlying vascular wall motion of superficial arteries and the chest wall. Data obtained from tissue phantoms suggested that the detected signals were related to intravascular pressure, an important clinical and physiological parameter. Unlike the conventional optical Doppler techniques that have been used to measure blood perfusion in skin layers and blood flow within superficial arteries, the present system was optimized to pick up skin vibrations. An optical interferometer with a 633-nm He:Ne laser was utilized to detect micrometer displacements of the skin surface. Motion velocity profiles of the skin surface near each superficial artery and auscultation points on a chest for the two heart valve sounds exhibited distinctive profiles. The theoretical and experimental results demonstrated that the system detected the velocity of skin movement, which is related to the time derivative of the pressure. The system also reduces the loading effect on the pulsation signals and heart sounds produced by the conventional piezoelectric vibration sensors. The system's sensitivity, which could be optimized further, was 366.2 μm/s for the present research. Overall, optical cardiovascular vibrometry has the potential to become a simple noninvasive approach to cardiovas-cular screening.
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In this paper we describe a new approach to the detection of human body electrical activity which has been made possible by recent advances in ultra-low-noise, ultra-high-input-impedance probes. As we demonstrate, these probes, which do not require a