Search

Seabirds interact with fishing vessels to consume fishing discards and baits, sometimes resulting in incidental capture (bycatch) and the death of the bird, which has clear conservation implications. To understand seabird-fishery interactions at large spatiotemporal scales, researchers are increasing their use of simultaneous seabird and fishing vessel tracking. However, vessel tracking data can contain gaps due to technical problems, illicit manipulation, or lack of adoption of tracking monitoring systems. These gaps might lead to underestimating the fishing effort and bycatch rates and jeopardize the effectiveness of marine conservation. We deployed bird-borne radar detector tags capable of recording radar signals from vessels. We placed tags on 88 shearwaters (Calonectris diomedea, Calonectris borealis, and Calonectris edwardsii) that forage in the northwestern Mediterranean Sea and the Canary Current Large Marine Ecosystem. We modeled vessel radar detections registered by the tags in relation to gridded automatic identification system (AIS) vessel tracking data to examine the spatiotemporal dynamics of seabird-vessel interactions and identify unreported fishing activity areas. Our models showed a moderate fit (area under the curve >0.7) to vessel tracking data, indicating a strong association of shearwaters to fishing vessels in major fishing grounds. Although in high-marine-traffic regions, radar detections were also driven by nonfishing vessels. The tags registered the presence of potential unregulated and unreported fishing vessels in West African waters, where merchant shipping is unusual but fishing activity is intense. Overall, bird-borne radar detectors showed areas and periods when the association of seabirds with legal and illegal fishing vessels was high. Bird-borne radar detectors could improve the focus of conservation efforts., (© 2024 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Heart rate is a crucial vital sign and a valuable indicator for assessing the physical and psychological condition of a target animal. Heart rate contributes to (1) fundamental information for cognitive research, (2) an indicator of psychological and physical stress, and (3) improving the animal welfare of captive animals, especially in nonhuman primate studies. Heart rate has been measured using a contact-type device; however, the device burdens the target animals and that there are risks associated with anesthesia during installation. This study explores the application of heartbeat measurement techniques using millimeter-wave radar, primarily developed for humans, as a remote and noninvasive method for measuring the heart rate of nonhuman primates. Through a measurement test conducted on two chimpanzees, we observed a remarkable correspondence between the peak frequency spectrum of heart rate estimated using millimeter-wave radar and the mean value obtained from electrocardiograph data, thereby validating the accuracy of the method. To the best of our knowledge, this is the first demonstration of the precise measurement of great apes' heart rate using millimeter-wave radar technology. Compared to heart rate measurement using video analysis, the method using millimeter-wave radar has the advantage that it is less susceptible to weather and lighting conditions and that measurement techniques for multiple individuals have been developed for human subjects, while its disadvantage is that validation of measurement from long distances has not been completed. Another disadvantage common to both methods is that measurement becomes difficult when the movement of the target individual is large. The possibility of noncontact measurement of heart rate in wild and captive primates will undoubtedly open up a new research area while taking animal welfare into consideration., (© 2024 The Author(s). American Journal of Primatology published by Wiley Periodicals LLC.)

An absorptive coding metasurface (ACM) is proposed to achieve radar cross section (RCS) reduction in this paper. In the design progress of the ACM, two different lossy coding elements are proposed at first, which can both be regarded as a composite composed of a two-layer resistive frequency selective surface (RFSS) and a polarization conversion metasurface (PCM). The two-layer RFSS has a certain wave-absorbing property due to ohmic loss. In addition, the PCM can achieve ultra-wideband linear polarization conversion, and the polarization-converted reflected waves in the two coding elements under the same incidence will differ by nearly 180° in phase because the sub-unit-cell structures in them are perpendicular to each other. Thus, based on the two lossy coding elements, the ACM is proposed, which can achieve ultra-wideband RCS reduction due to absorption and phase cancelation. Numerical simulations demonstrate that the RCS of the ACM under normal incidence can be reduced more than 10 dB in the ultra-wide frequency band from 6.9 to 41.2 GHz with a relative bandwidth of 142.6%. Moreover, it has the advantages of polarization-insensitivity and wide incident angle. Finally, one effective experimental verification is carried out, and a reasonable agreement is observed between the simulation and experimental results. [ABSTRACT FROM AUTHOR]

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

Study Objectives: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards., Methods: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDI PSG_TST ), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDI PSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDI PSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared., Results: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDI PSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDI PSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDI PSG_TST : 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDI PSG_TST : 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%., Conclusions: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work., Citation: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med . 2024;20(8):1267-1277., (© 2024 American Academy of Sleep Medicine.)

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

Human action recognition based on optical and infrared video data is greatly affected by the environment, and feature extraction in traditional machine learning classification methods is complex; therefore, this paper proposes a method for human action recognition using Frequency Modulated Continuous Wave (FMCW) radar based on an asymmetric convolutional residual network. First, the radar echo data are analyzed and processed to extract the micro-Doppler time domain spectrograms of different actions. Second, a strategy combining asymmetric convolution and the Mish activation function is adopted in the residual block of the ResNet18 network to address the limitations of linear and nonlinear transformations in the residual block for micro-Doppler spectrum recognition. This approach aims to enhance the network's ability to learn features effectively. Finally, the Improved Convolutional Block Attention Module (ICBAM) is integrated into the residual block to enhance the model's attention and comprehension of input data. The experimental results demonstrate that the proposed method achieves a high accuracy of 98.28% in action recognition and classification within complex scenes, surpassing classic deep learning approaches. Moreover, this method significantly improves the recognition accuracy for actions with similar micro-Doppler features and demonstrates excellent anti-noise recognition performance.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range-velocity-time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland-Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.

To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [-4.78, 4.78] beats per minute, and a consistency error of -0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Concerns about perceived widespread declines in insect numbers have led to recognition of a requirement for long-term monitoring of insect biodiversity. Here we examine whether an existing, radar-based, insect monitoring system developed for research on insect migration could be adapted to this role. The radar detects individual larger (greater than 10 mg) insects flying at heights of 150-2550 m and estimates their size and mass. It operates automatically and almost continuously through both day and night. Accumulation of data over a 'half-month' (approx. 15 days) averages out weather effects and broadens the source area of the wind-borne observation sample. Insect counts are scaled or interpolated to compensate for missed observations; adjustment for variation of detectability with range and insect size is also possible. Size distributions for individual days and nights exhibit distinct peaks, representing different insect types, and Simpson and Shannon-Wiener indices of biodiversity are calculated from these. Half-month count, biomass and index statistics exhibit variations associated with the annual cycle and year to year changes that can be attributed to drought and periods of high rainfall. While species-based biodiversity measures cannot be provided, the radar's capacity to estimate insect biomass over a wide area indicates utility for tracking insect population sizes. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.

Heart rate (HR) and respiration rate (RR) play an important role in the study of complex behaviors and their physiological correlations in non-human primates (NHPs). However, collecting HR and RR information is often challenging, involving either invasive implants or tedious behavioral training, and there are currently few established simple and non-invasive techniques for HR and RR measurement in NHPs owing to their stress response or indocility. In this study, we employed a frequency-modulated continuous wave (FMCW) radar to design a novel contactless HR and RR monitoring system. The designed system can estimate HR and RR in real time by placing the FMCW radar on the cage and facing the chest of both awake and anesthetized macaques, the NHP investigated in this study. Experimental results show that the proposed method outperforms existing methods, with averaged absolute errors between the reference monitor and radar estimates of 0.77 beats per minute (bpm) and 1.29 respirations per minute (rpm) for HR and RR, respectively. In summary, we believe that the proposed non-invasive and contactless estimation method could be generalized as a HR and RR monitoring tool for NHPs. Furthermore, after modifying the radar signal-processing algorithms, it also shows promise for applications in other experimental animals for animal welfare, behavioral, neurological, and ethological research., (© 2024. The Author(s).)

Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for measuring respiratory movements during swallowing using millimeter wave radar to detect these pauses. The experiment involved 20 healthy adult participants. The results showed a correlation of 0.71 with the measurement data obtained from a band-type sensor used as a reference, demonstrating the potential to measure chest movements associated with respiration using a non-contact method. Additionally, temporary respiratory pauses caused by swallowing were confirmed by the measured data. Furthermore, using machine learning, the presence of respiring alone was detected with an accuracy of 88.5%, which is higher than that reported in previous studies. Respiring and temporary respiratory pauses caused by swallowing were also detected, with a macro-averaged F1 score of 66.4%. Although there is room for improvement in temporary pause detection, this study demonstrates the potential for measuring respiratory movements during swallowing using millimeter wave radar and a machine learning method.

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery., Competing Interests: Declaration of competing interest Authors have no conflict of interest to declare., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Aiming at the characteristics that the signal noise ratio (SNR) gradually decreases from the near to far range of the swath, an adaptive phase filtering algorithm based on Goldstein filtering and combined with multiple quality-guided graphs was proposed. Firstly, the components used to determine the filtering parameters were obtained through residue density, pseudo-coherence coefficient and pseudo-SNR, the three quality-guided graphs. Then, the filter parameters were calculated by weighting the three components. Finally, the size of filtering window was determined according to the account of residues, and the interferometric phase noise was removed in frequency domain. Simulated data, TSX/TDX data and airborne interferometric imaging radar altimeter data were used to verify the performance of the new algorithm. Compared with the results of Goldstein filtering and its improved algorithms, the results showed that the proposed algorithm can effectively filter out phase noise while maintaining the edge characteristics of interferometric fringe. The section of filtering result can well match with the section of simulated pure interfeometric phase. Moreover, the algorithm proposed in this paper can effectively remove the noise in the interferogram of TSX/TDX sea ice data, and the residues' filtering rate was above 86%, which can effectively remove the phase residues of the sea ice surface while maintaining the characteristics of the sea ice edge. Experimental results showed that the new algorithm provides an effective phase noise filtering method for imaging radar altimeter data processing., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Nguyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Purpose: The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI)., Methods: From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring., Results: The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was r p  = 0.94 and r p  = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%., Conclusion: The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09., (© 2024. The Author(s).)

This work investigated the annual variations in dry snow (DSRZ) and wet snow radar zones (WSRZ) in the north of the Antarctic Peninsula between 2015-2023. A specific code for snow zone detection on Sentinel-1 images was created on Google Earth Engine by combining the CryoSat-2 digital elevation model and air temperature data from ERA5. Regions with backscatter coefficients (σ⁰) values exceeding -6.5 dB were considered the extent of surface melt occurrence, and the dry snow line was considered to coincide with the -11 °C isotherm of the average annual air temperature. The annual variation in WSRZ exhibited moderate correlations with annual average air temperature, total precipitation, and the sum of annual degree-days. However, statistical tests indicated low determination coefficients and no significant trend values in DSRZ behavior with atmospheric variables. The results of reducing DSRZ area for 2019/2020 and 2020/2021 compared to 2018/2018 indicated the upward in dry zone line in this AP region. The methodology demonstrated its efficacy for both quantitative and qualitative analyses of data obtained in digital processing environments, allowing for the large-scale spatial and temporal variations monitoring and for the understanding changes in glacier mass loss.

Study Objectives: Night-to-night variability of sleep-disordered breathing limits the diagnostic accuracy of a single measurement. Multiple recordings using a reliable, affordable method could reduce the uncertainty and avoid misdiagnosis, which could be possible with radar-based home sleep apnea testing (HSAT)., Methods: We recruited consecutive patients with suspected sleep-disordered breathing and performed contactless radar-based HSAT with automated scoring (Sleepiz One; Sleepiz AG, Zurich, Switzerland) over 10 nights. During the first night, patients were simultaneously measured with peripheral arterial tonometry., Results: Twenty-four of the 28 included patients could achieve a minimum of 4 measurements. The failure rate was 16% (37 of 238 measurements). The apnea-hypopnea index (AHI) and oxygen desaturation index were consistently lower with radar-based HSAT compared with peripheral arterial tonometry. The variability of the AHI was considerable, with a standard error of measurement of 5.2 events/h (95% confidence interval [CI]: 4.6-5.7 events/h) and a minimal detectable difference of 14.4 events/h (95% CI: 12.7-15.9 events/h). Alcohol consumption partially accounted for the variability, with an AHI increase of 1.7 events/h (95% CI: 0.6-2.8 events/h) for each standard drink. Based on a single measurement, 17% of patients were misdiagnosed and 32% were misclassified for sleep-disordered breathing severity. After 5 measurements, the mean AHI of the measured nights stabilized with no evidence of substantial changes with additional measurements., Conclusions: Night-to-night variability is considerable and stable over 10 nights. HSAT using radar-based methods over multiple nights is feasible and well tolerated by patients. It could offer lower costs and allow for multiple-night testing to increase accuracy. However, validation and reducing the failure rate are necessary for implementation in the clinical routine., Clinical Trial Registration: Registry: ClinicalTrials.gov; Name: Recording of Multiple Nights Using a New Contactless Device (Sleepiz One Connect) in Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/study/NCT05134402; Identifier: NCT05134402., Citation: Tschopp S, Borner U, Caversaccio M, Tschopp K. Long-term night-to-night variability of sleep-disordered breathing using a radar-based home sleep apnea test: a prospective cohort study. J Clin Sleep Med . 2024;20(7):1079-1086., (© 2024 American Academy of Sleep Medicine.)

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Krapivnitckaia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)