Search Results (213)

Non-contact monitoring of human vital signs using microwave radar has attracted increasing attention due to its capability to operate unobtrusively and through clothing or light obstacles. In vector network analyzer (VNA)-based radar systems, vital signs can be extracted from phase variations in the forward transmission coefficient $S_{21}$, whose sensitivity strongly depends on the electromagnetic performance of the antenna system. This work presents the design, optimization, fabrication, and experimental validation of a high-gain 12 GHz 4 × 4 microstrip patch antenna array specifically developed for phase-based vital signs monitoring. The antenna array was progressively optimized through coaxial feeding, slot-based impedance control, stepped transmission line matching, and mitered bends, achieving a simulated gain of 17.8 dBi, a measured gain of 17.06 dBi, a reflection coefficient of −26 dB at 12 GHz, and a total efficiency close to 74%. The antenna performance was experimentally validated in an anechoic chamber and subsequently integrated into a continuous-wave VNA-based radar system. Comparative measurements were conducted against a commercial biconical antenna, a single patch radiator, and an MIMO antenna under identical conditions. Results demonstrate that while respiration can be detected with moderate-gain antennas, reliable heartbeat detection requires high-gain, narrow-beam antennas to enhance phase sensitivity and suppress environmental clutter. The proposed array significantly improves pulse detectability in the (1–1.5) Hz band without relying on advanced signal processing. These findings highlight the critical role of antenna design in $S_{21}$-based biomedical radar systems and provide practical design guidelines for high-sensitivity non-contact vital signs monitoring. Full article

Continuous heart rate monitoring is crucial for early cardiovascular disease detection. To overcome the discomfort and limitations of ECG in home settings, we propose a multimodal temporal fusion network (MM-TFNet) that integrates ballistocardiography (BCG) and photoplethysmography (PPG) signals. The network extracts temporal features from BCG and PPG signals through temporal convolutional networks (TCNs) and bidirectional long short-term memory networks (BiLSTMs), respectively, achieving cross-modal dynamic fusion at the feature level. First, bimodal features are projected into a unified dimensional space through fully connected layers. Subsequently, a cross-modal attention weight matrix is constructed for adaptive learning of the complementary correlation between BCG mechanical vibration and PPG volumetric flow features. Combined with dynamic focusing on key heartbeat waveforms through multi-head self-attention (MHSA), the model’s robustness under dynamic activity states is significantly enhanced. Experimental validation using a publicly available BCG-PPG-ECG simultaneous acquisition dataset comprising 40 subjects demonstrates that the model achieves excellent performance with a mean absolute error (MAE) of 0.88 BPM in heart rate prediction tasks, outperforming current mainstream deep learning methods. This study provides theoretical foundations and engineering guidance for developing contactless, low-power, edge-deployable home health monitoring systems, demonstrating the broad application potential of multimodal fusion methods in complex physiological signal analysis. Full article

This paper proposes a low-cost measurement system that can be used to perform simultaneous blood pressure (BP) and breathing (BR) measurements. Regarding BP measurements, the main parameters that are accessed include systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure blood pressure (MAP), and heartbeat rate (HR). Concerning BR measurements, the main parameters that are accessed include the inspiration period and amplitude (IPA), the expiration period and amplitude (EPA), and the breathing rate (BR), as well as the statistical and standard deviation of all these parameters. The dual measurement capability of the proposed measurement system is very important since blood pressure and breathing parameters are not statistically independent and it is possible to obtain additional and valuable clinical information from the information provided by both biomedical variables when measured simultaneously. The analysis of the correlation between these variables is particularly important after performing intensive physical exercises, since it enables cardiac rehabilitation assessment, pre-surgical risk evaluation, detection of silent ischemia, and monitoring of chronic diseases recovery, among others. Regarding the performance evaluation of the proposed biomedical device, a prototype of the measurement system was developed, tested, and calibrated. Several experimental tests were carried out to evaluate the performance of the proposed measurement system and to obtain the correlation coefficients between different blood pressure and breathing parameters. The tests were based on a statistically significant number of measurements that were performed with a population that integrated twenty students in two groups with different habits of physical exercise practice but subjected to a set of common physical exercises, with graduated intensity levels. Full article

Background and Objectives: Ectopic pregnancy (EP) is a life-threatening medical and surgical condition. Tubal stump EPs and heterotopic pregnancies can occur after in vitro fertilization-embryo transfer (IVF-ET), even after salpingectomy. The purpose of this study is to investigate the risk factors, diagnosis, and treatment of tubal stump EPs after IVF-ET in patients with prior salpingectomy or adnexectomy. We also aim to evaluate the intrauterine pregnancy (IUP) outcome in cases of heterotopic pregnancy in this population. Materials and Methods: This systematic review (PROSPERO CRD42023352959) followed PRISMA guidelines. A literature search of MEDLINE^®, Scopus, Web of Science, and clinicaltrials.gov was conducted on 30 April 2024. We included studies on tubal stump EP after IVF-ET in patients with previous salpingectomy or adnexectomy and created a qualitative summary. Results: We included 40 studies reporting on 57 patients (58 EP episodes). Most patients (69.0%) had prior bilateral salpingectomy. Tubal rupture occurred in 69.6% of cases, with 69.0% of these cases reporting hemoperitoneum. Abdominal pain was the most frequent symptom (71.7%). Heterotopic pregnancy occurred in 60.0% of cases (82.7% singletons). The IUP outcome was delivery in 81.9% of cases, with 95.5% of singletons delivering at term, compared with 40.0% of twins. The surgical approach (laparoscopy vs. laparotomy) did not change the IUP outcome. Tubal stump excision (74.1%) was the most common treatment. Overall, the certainty of the evidence was judged as moderate to very low according to the GRADE-CERQual approach, mainly due to small sample sizes, observational designs, and heterogeneity among studies. Conclusions: This review, the first on this topic, provides key data for counselling patients with a tubal stump heterotopic pregnancy. Despite its rarity, close follow-up until 8–10 weeks is recommended for IVF-ET patients with positive β-hCG, monitoring for abdominal pain. Successful management (expectant, medical, or surgical) should be guided by β-hCG levels and ultrasound findings (e.g., absence of heartbeat). Medical treatment shows encouraging obstetric outcomes and warrants further research. Full article

The field of experimental radiooncology and the quality assessment (QA) aimed at patient safety both profit from the utilisation of biomimetic principles. The work with phantoms based on biological structures of animals or humans, utilising the principles of anatomic mimicry, has a long tradition in radiotherapy research. When phantoms are produced from tissue-equivalent materials, they mimic the radiological properties of tissues and organs, allowing researchers and clinicians to study dose distribution and optimise treatment plans without exposing real patients to radiation. Biomechanical mimicry would take this a step further by creating phantoms that replicate the movement and deformation of organs during physiological movement, such as heartbeat or breathing, enabling a more accurate simulation of dynamic treatment scenarios. Bioinspired sensor technologies, such as artificial skin or integrated detectors, can be used to monitor radiation exposure, organ motion or temperature changes during therapy with high precision. The utility of such a phantom could be further enhanced by creating a realistic tumour microenvironment as an irradiation target, following the principles of microenvironmental biomimicry. Thus, biomimetic strategies can be exploited in the validation of radiotherapy technologies and open new perspectives for adaptive radiotherapy and real-time monitoring. Full article

The electrocardiogram (ECG) is a vital diagnostic tool used to monitor heart activity and detect cardiac abnormalities, such as arrhythmias. Accurate classification of normal and abnormal heartbeats is essential for effective diagnosis and treatment. Traditional deep learning methods for automated ECG classification primarily focus on reconstructing the original ECG signal and detecting anomalies based on reconstruction errors, which represent abnormal features. However, these approaches struggle with unseen or underrepresented abnormalities in the training data. In addition, other methods rely on manual feature extraction, which can introduce bias and limit their adaptability to new datasets. To overcome this problem, this study proposes an end-to-end model called ECG-CBA, which integrates the convolutional neural networks (CNNs), bidirectional long short-term memory networks (Bi-LSTM), and a multi-head Attention mechanism. ECG-CBA model learns discriminative features directly from the original dataset rather than relying on feature extraction or signal reconstruction. This enables higher accuracy and reliability in detecting and classifying anomalies. The CNN extracts local spatial features from raw ECG signals, while the Bi-LSTM captures the temporal dependencies in sequential data. An attention mechanism enables the model to primarily focus on critical segments of the ECG, thereby improving classification performance. The proposed model is trained on normal and abnormal ECG signals for binary classification. The ECG-CBA model demonstrates strong performance on the ECG5000 and MIT-BIH datasets, achieving accuracies of 99.60% and 98.80%, respectively. The model surpasses traditional methods across key metrics, including sensitivity, specificity, and overall classification accuracy. This offers a robust and interpretable solution for both ECG-based anomaly detection and cardiac abnormality classification. Full article

This paper presents an optimised signal processing framework for contactless physiological monitoring using Frequency Modulated Continuous Wave (FMCW) radar within automotive environments. This research focuses on enhancing heart rate (HR) and heart rate variability (HRV) detection from radar signals by integrating radar placement optimisation and advanced phase-based processing techniques. Optimal radar placement was evaluated through Signal-to-Clutter Ratio (SCR) analysis, conducted with multiple human participants in both laboratory and dynamic driving simulator experimental conditions, to determine the optimal in-vehicle location for signal acquisition. An effective processing pipeline was developed, incorporating background subtraction, range bin selection, bandpass filtering, and phase unwrapping. These techniques facilitated the reliable extraction of inter-beat intervals and heartbeat peaks from the phase signal without the need for contact-based sensors. The framework was evaluated using a Walabot FMCW radar module against ground truth HR signals, demonstrating consistent and repeatable results under baseline and mild motion conditions. In subsequent work, this framework was extended with deep learning methods, where radar-derived HR and HRV were benchmarked against research-grade ECG and achieved over 90% accuracy, further reinforcing the robustness and reliability of the approach. Together, these findings confirm that carefully guided radar positioning and robust signal processing can enable accurate and practical in-cabin physiological monitoring, offering a scalable solution for integration in future intelligent vehicle and driver monitoring systems. Full article

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

A non-intrusive mattress based on an optical-fiber Michelson interferometer is designed for daily sleep monitoring. The optical phase signal of the optical-fiber Michelson interferometer caused by the heartbeat and respiration is demodulated by the phase-generated carrier (PGC) method. The physiological signals and vital indicators including heart rate (HR), respiration rate (RR), and signal energy (SE) are extracted from the optical phase by algorithmic processing. A series of experiments are conducted to confirm the feasibility of the mattress for sleep monitoring. The mattress not only can achieve HR and RR counting, but also can record the waveform of the sleep-induced signal accurately. The body states can also be distinguished by the SE. In an all-night sleep monitoring experiment, the HR measured by the mattress is compared with the HR measured by a commercial smart band, showing a maximum error of 6 bpm (beat per minute). The designed mattress based on an optical-fiber Michelson interferometer shows good performance and great potential in non-intrusive sleep monitoring. Full article

The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. Full article

Danionella, a transparent freshwater species belonging to the Cyprinidae family, has emerged as a valuable model organism in biological and medical research due to its optical transparency. The cardiovascular system of Danionella larvae provides a unique opportunity for non-invasive heart rate monitoring in aquatic animals. Traditional approaches for evaluating larval heart rate often require manual or semi-automated definition of the cardiac region in video recordings. In this study, we developed a simplified heart rate monitoring system that estimates heartbeat activity by analyzing interframe luminance differences in video sequences of Danionella larvae. Our system successfully measured heart rates in the range of 150–155 beats per minute (bpm), consistent with previous findings reporting rates between 140 and 200 bpm. The non-invasive nature of this method offers significant advantages for high-throughput screening and long-term physiological monitoring. Furthermore, this system has potential applications in evaluating environmental stressors, supporting survival and health assessments, and guiding habitat management strategies to ensure stable populations of adult fish in both natural and laboratory settings. Full article

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. Full article

The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations. This study proposes a method for vital sign detection using an integrated sensing and communication (ISAC) system where a unified orthogonal frequency division multiplexing (OFDM) signal was adopted, and it is capable of sensing life signs and carrying out communication simultaneously. An ISAC demonstration system based on software-defined radios (SDRs) was initiated to detect respiratory and heartbeat rates while maintaining communication capability in a typical office environment. The specially designed OFDM signals were transmitted, reflected from a human subject, received, and processed to estimate the micro-Doppler effect induced by the breathing and heartbeat of the human in the environment. According to the results, vital signs, including respiration and heartbeat rates, have been accurately detected by post-processing the reflected OFDM signals with a 1 MHz bandwidth, confirmed with conventional contact-based detection approaches. The potential of dual-function capability of OFDM signals for sensing purposes has been verified. The principle and method developed can be applied in wider ISAC systems for search and rescue purposes while maintaining communication links. Full article

In the context of Doppler radar, studies have examined the changes in the phase shift of the S₂₁ transmission coefficient related to minute movements of the human chest as a response to breathing or heartbeat. Detecting human vital signs remains a challenge, especially when obstacles interfere with the attempt to detect the presence of life. The sensitivity of a measurement system’s perception of vital signs is highly dependent on the monitoring systems and antennas that are used. The current work proposes a computational approach that aims to extract an empirical law of the dependence of the phase shift of the transmission coefficient (S₂₁) on the sensitivity at reception, based upon a set of four parameters. These variables are as follows: (a) the frequency of the continuous wave utilized; (b) the antenna type and its gain/directivity; (c) the electric field strength distribution on the chest surface (and its average value); and (d) the type of material (dielectric properties) impacted by the incident wave. The investigated frequency range is (1–20) GHz, while the simulations are generated using a doublet of dipole or gain-convenient identical Yagi antennas. The chest surface is represented by a planar rectangle that moves along a path of only 3 mm, with a step of 0.3 mm, mimicking respiration movement. The antenna–target system is modeled in the computational space in each new situation considered. The statistics illustrate the multiple regression function, empirically extracted. This enables the subsequent building of a continuous-wave bio-radar Doppler system with controlled and improved sensitivity. Full article

A new modulation scheme for frequency-modulated continuous-wave (FMCW) radars with millimeter-level target motion detection capability is presented. The proposed radar scheme is free from the synchronization constraint and exhibits low sensitivity to internal parasitic mutual coupling, thus significantly reducing its design complexity without worsening its performance in terms of accuracy and operating ranges. Alternatively to canonical FMCW radars, which exploit chirp signals with triangular or sawtooth-like frequency variation, a radar based on a sinusoidal frequency modulation, which does not require specific synchronization procedures to achieve accurate motion detection even at a short distance from the radar, was developed. Both numerical and experimental results, performed with a 24 GHz radar, have shown the suitability of the proposed modulation scheme for monitoring very small target movements, consistent with those typically exhibited by the human thorax during basic vital activities (heartbeat and respiration). This makes the proposed radar scheme a suitable solution for contactless heart and breath rate monitoring. Full article