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Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV). In this paper, we propose a simple yet effective method to measure VS for medical radar, named 3M method. Specifically, our method firstly introduces the Markov-Gauss model to obtain the recursive expression of the echo phases carrying VS, and secondly derive a simple observation equation (SOE) to reflect the relationship between the observed signal and VS of radar measurement. Thirdly, the aforementioned Markov-Gauss model and SOE are fused by Kalman filter to measure VS with accurate estimation. The 3M method demonstrates an elegant structure, low complexity and excellent features introduced by Kalman filter. Simulation results show the superiority of 3M over other methods. Then, we conduct extensive experiments with insightful visualizations to validate the effectiveness of the 3M method. Comparative results on different scenarios illustrate that the 3M method not only achieves state-of-the-art VS measurement performance but also expresses robust properties to HRV analysis.

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.

Annually, a significant number of premature infants suffer from apnea, which can easily cause a drop in oxygen saturation levels, leading to hypoxia. However, infant cardiopulmonary monitoring using conventional methods often necessitates skin contact, and they are not suitable for long-term monitoring. This article introduces a non-contact technique for infant cardiopulmonary monitoring and an adjustable apnea detection algorithm. These are developed using a custom-designed K-band continuous-wave biomedical radar sensor system, which features a DC-coupled adaptive digital tuning function. By using radar technology to detect chest motions without physical contact, it is feasible to extract significant biological information regarding an infant's respiration and heartbeat. The proposed algorithm utilizes an adaptively adjusted threshold and personalized apnea warning time to automatically measure the total number of apneic events and their respective durations. Experiments have been conducted in clinical environment, demonstrating that both the accurate cardiopulmonary signals and the apneas of varying durations can be effectively monitored using this method, which suggest that the proposed technique has potential applications both inside and outside of clinical settings.

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Non-contact technology for monitoring the vital signs of multiple individuals, such as respiration and heartbeat, has been investigated in recent years due to the rising cardiopulmonary morbidity, the risk of disease transmission, and the heavy burden on medical staff. Frequency-modulated continuous wave (FMCW) radars have shown great promise in meeting these needs, even using a single-input-single-output (SISO) setup. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via SISO FMCW radar, are based on simplistic models and present difficulties in coping with noisy environments containing multiple objects. In this work, we first develop an extended model for multi-person NCVSM via SISO FMCW radar. Then, by utilizing the sparse nature of the modeled signals in conjunction with human-typical cardiopulmonary features, we present accurate localization and NCVSM of multiple individuals in a cluttered scenario, even with only a single channel. Specifically, we provide a joint-sparse recovery mechanism to localize people and develop a robust method for NCVSM called Vital Signs-based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for the rates of respiration and heartbeat over high-resolution grids corresponding to human cardiopulmonary activity. The advantages of our method are illustrated through examples that combine the proposed model with in-vivo data of 30 individuals. We demonstrate accurate human localization in a noisy scenario that includes both static and vibrating objects and show that our VSDR approach outperforms existing NCVSM techniques based on several statistical metrics. The findings support the widespread use of FMCW radars with the proposed algorithms in healthcare.

With the rapid development towards smart Internet of Things (IoT), detection of human activity has become essential in a variety of applications. Various radio-frequency (RF) sensing technologies, such as WiFi, Radio-Frequency Identification (RFID), and Frequency-Modulated Continuous Wave (FMCW) radar, have been utilized for non-invasive human activity recognition (HAR). It will be highly desirable to develop a HAR solution that can work with different types of RF technologies, such that the cost and the barrier of wide deployment can both be greatly reduced, and more robust performance can be achieved by utilizing the complementary RF sensory data. In this paper, we propose a technology-agnostic approach for RF-based HAR, termed TARF, which works with several different RF sensing technologies. A novel data generalization technique is proposed to mitigate the disparity in measured data from different RF devices. A domain adversarial neural network is proposed to combat the interference from various RF sensing technologies. The performance of the proposed system is evaluated with experiments using four different RF sensing technologies. TARF is shown to outperform the state-of-the-art Convolutional Neural Network (CNN)-based solution with considerable gains.

Restricted by observation conditions, some scarce targets in the synthetic aperture radar (SAR) image only have a few samples, making effective classification a challenging task. Although few-shot SAR target classification methods originated from meta-learning have made great breakthroughs recently, they only focus on object-level (global) feature extraction while ignoring part-level (local) features, resulting in degraded performance in fine-grained classification. To tackle this issue, a novel few-shot fine-grained classification framework, dubbed as HENC, is proposed in this article. In HENC, the hierarchical embedding network (HEN) is designed for the extraction of multi-scale features from both object-level and part-level. In addition, scale-channels are constructed to realize joint inference of multi-scale features. Moreover, it is observed that the existing meta-learning-based method only implicitly utilize the information of multiple base categories to construct the feature space of novel categories, resulting in scattered feature distribution and large deviation during novel center estimation. In view of this, the center calibration algorithm is proposed to explore the center information of base categories and explicitly calibrate the novel centers by dragging them closer to the real ones. Experimental results on two open benchmark datasets demonstrate that the HENC significantly improves the classification accuracy for SAR targets.

Objective: Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology., Methods: Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming., Results: The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful., Conclusion: Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging., Significance: This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings.

Objective: This paper presents a Proof-of-Concept (POC) design and implementation of a biosensing and communication system that can be used for biotelemetry in neural and Gastrointestinal (GI) applications., Methods: Our proposed system is based on backscattering from a semi-passive Radio-Frequency-Identification-Device (RFID) implemented using an Application Specific Integrated Circuit (ASIC) in which electronic switching between transistor gates in high and low states create an impedance difference, thereby effectively changing the ASIC's Radar Cross Section (RCS) and thus modulating its backscattered field. The ASIC is used in conjunction with a biosensor to measure and transmit vital signs from within the body. With this system, we conducted backscatter propagation experiments through different biological and phantom tissues (in ex-vivo and in-vitro) in the GI and neural environments., Results: Our results show that the backscattered waveforms can penetrate tissues of various compositions and thicknesses with power received at distances of up to 55 cm away from the RFID ASIC. Furthermore, results from single- and multi-bit biotelemetry measurements showed a high signal fidelity with low Bit-Error-Rate (BER) while being able to resolve varying tissue temperatures measured by the biosensor in our system., Conclusion: We realized a POC system in which on-chip transistor switching in an ASIC can be used to achieve backscatter communication and biosensing. This system is deployable in neural and GI applications., Significance: Our findings in this work will provide an important practical basis for the design and development of RFID ASIC for biosensing and biotelemetry in medical applications.

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical healthcare applications where reliability and robustness are crucial. Multipath reflections, originated from one person, combine with the direct signals and multipaths of other people and stationary objects, thus jeopardizing individual vital signs extraction and localization. This work focuses on tackling indoor multipath propagation., Methods: We describe a methodology, based on accurate models of the indoor multipaths and of the radar signals, that enables separating the undesired multipaths from desired signals of multiple individuals, removing a key obstacle to real-world contactless vital signs monitoring and localization., Results: We also demonstrated it by accurately measure individual heart rates, respiration rates, and absolute distances (range information) of paired volunteers in a challenging real-world office setting., Conclusion: The approach, validated using a frequency-modulated continuous wave (FMCW) radar, was shown to function in an indoor environment where radar signals are severely affected by multipath reflections., Significance: Practical applications arise for health care, assisted living, geriatric and quarantine medicine, rescue and security purposes.

Manual scoring of sleep stages from polysomnography (PSG) records is essential to understand the sleep quality and architecture. Since the PSG requires specialized personnel, a lab environment, and uncomfortable sensors, non-contact sleep staging methods based on machine learning techniques have been investigated over the past years. In this study, we propose an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model for automatic sleep stage scoring using an impulse-radio ultra-wideband (IR-UWB) radar which can remotely detect vital signs. Sixty-five young (30.0 ± 8.6 yrs.) and healthy volunteers underwent nocturnal PSG and IR-UWB radar measurement simultaneously; From 51 recordings, 26 were used for training, 8 for validation, and 17 for testing. Sixteen features including movement-, respiration-, and heart rate variability-related indices were extracted from the raw IR-UWB signals in each 30-s epoch. Sleep stage classification performances of Attention Bi-LSTM model with optimized hyperparameters were evaluated and compared with those of conventional LSTM networks for same test dataset. In the results, we achieved an accuracy of 82.6 ± 6.7% and a Cohen's kappa coefficient of 0.73 ± 0.11 in the classification of wake stage, REM sleep, light (N1+N2) sleep, and deep (N3) sleep which is significantly higher than the conventional LSTM networks (p < 0.01). Moreover, the classification performances were higher than those reported in comparative studies, demonstrating the effectiveness of the attention mechanism coupled with bi-LSTM networks for the sleep staging using cardiorespiratory signals.

Objective: Non-contact sensing of seismocardiogram (SCG) signals through a microwave Doppler radar is promising for biomedical applications. However, the delineation of fiducial points for radar SCG still relies on concurrent ECG which requires a contact sensor and limits the complete non-contact detection of SCG., Methods: Instead of ECG, a new reference signal, the radar displacement signal of heartbeat (RDH), was derived through the complex Fourier transform and the band pass filtering of the radar signal. The RDH signal was used to locate each cardiac cycle and mask the systolic profile, which was further used to detect an important fiducial point, aortic valve opening (AO). The beat-to-beat interval was estimated from AO-AO interval and compared with the gold standard, ECG R-to-R interval., Results: For the 22 subjects in the study, the evaluation of the AOs detected by RDH (AO RDH ) shows the average detection ratio can reach 90%, indicating a high ratio of the AO RDH that are exactly the same as AO detected using the ECG R-wave (AO ECG ). Additionally, the left ventricular ejection time (LVET) values estimated from the ensemble averaged radar waveform through AO RDH segmentation are within 2 ms of those through AO ECG segmentation, for all the detected subjects. Further analysis demonstrates that the beat-to-beat intervals calculated from AO RDH have an average root-mean-square-deviation (RMSD) of 53.73 ms when compared with ECG R-to-R intervals, and have an average RMSD of 23.47 ms after removing the beats in which AO cannot be identified., Conclusions: Radar signal RDH can be used as a reference signal to delineate fiducial points for non-contact radar SCG signals., Significance: This study can be applied to develop complete non-contact sensing of SCG and monitoring of vital signs, where contact-based SCG is not feasible.

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.

The scattering signatures of a synthetic aperture radar (SAR) target image will be highly sensitive to different azimuth angles/poses, which aggravates the demand for training samples in learning-based SAR image automatic target recognition (ATR) algorithms, and makes SAR ATR a more challenging task. This paper develops a novel rotation awareness-based learning framework termed RotANet for SAR ATR under the condition of limited training samples. First, we propose an encoding scheme to characterize the rotational pattern of pose variations among intra-class targets. These targets will constitute several ordered sequences with different rotational patterns via permutations. By further exploiting the intrinsic relation constraints among these sequences as the supervision, we develop a novel self-supervised task which makes RotANet learn to predict the rotational pattern of a baseline sequence and then autonomously generalize this ability to the others without external supervision. Therefore, this task essentially contains a learning and self-validation process to achieve human-like rotation awareness, and it serves as a task-induced prior to regularize the learned feature domain of RotANet in conjunction with an individual target recognition task to improve the generalization ability of the features. Extensive experiments on moving and stationary target acquisition and recognition benchmark database demonstrate the effectiveness of our proposed framework. Compared with other state-of-the-art SAR ATR algorithms, RotANet will remarkably improve the recognition accuracy especially in the case of very limited training samples without performing any other data augmentation strategy.

The 3.5 GHz citizens broadband radio service (CBRS) band in the U.S. is a key portion of mid-band spectrum shared between commercial operators and existing federal and non-federal incumbents. To protect the federal incumbents from harmful interference, a spectrum access system (SAS) is required to use a common, standardized algorithm, called the move list algorithm, to suspend transmissions of some CBRS devices (CBSDs) on channels in which the incumbent becomes active. However, the current reference move list implementation used for SAS testing is non-deterministic in that it uses a Monte Carlo estimate of the 95 th percentile of the aggregate interference from CBSDs to the incumbent. This leads to uncertainty in move list results and in the aggregate interference check of the test. This paper uses upper and lower bounds on the aggregate interference distribution to compute deterministic move lists. These include the reference move list used by the testing system and an operational move list used by the SAS itself. We evaluate the performance of the proposed deterministic move lists using reference implementations of the standards and simulated CBSD deployments in the vicinity of federal incumbent dynamic protection areas.

Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this paper is concerned with solving the intra motion category classification problem of gait recognition., Methods: New gait classification approaches utilizing physical features, subspace features, and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological, and assisted walks., Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition, which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual, a classification accuracy on the order of 80% can be expected., Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy., Significance: Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.

Heart rate (HR) variability indicates health condition and mental stress. The development of non-contact HR monitoring techniques with Doppler radar is attracting great attention. However, the performance of heartbeat detection via radar signals easily degrades due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct the high-resolution spectrum of heartbeat by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function and penalize the sparse constraint on updating the spectrum, a more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises of different strengths caused by subjects' movements, an adaptive regularization parameter is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of the location of the spectral peak associated with the HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement in accuracy compared with existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute when subjects type on a laptop.

Spectrum sharing in the 3.5 GHz band between commercial and government users along U.S. coastal areas depends on an environmental sensing capability (ESC)-that is, a network of radio frequency sensors and a decision system-to detect the presence of incumbent shipborne radar systems and trigger protective measures, as needed. It is well known that the sensitivity of these sensors depends on the aggregate interference generated by commercial systems to the incumbent radar receivers, but to date no comprehensive study has been made of the aggregate interference in realistic scenarios and its impact on the requirement for detection of the radar signal. This paper presents systematic methods for determining the placement of ESC sensors and their detection thresholds to adequately protect incumbent shipborne radar systems from harmful interference. Using terrain-based propagation models and a population-based deployment model, the analysis finds the offshore distances at which protection must be triggered and relates these to the detection levels of coastline sensors. We further show that sensor placement is a form of the well-known set cover problem, which has been shown to be NP-complete, and demonstrate practical solutions achieved with a greedy algorithm. Results show detection thresholds to be as much as 22 dB lower than required by current industry standards. The methodology and results presented in this paper can be used by ESC operators for planning and deployment of sensors and by regulators for testing sensor performance.

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Objective: We have developed and tested a new architecture for pulse transit time (PTT) estimation at the central arteries using electrical bioimpedance, electrocardiogram, and continuous wave radar to estimate cuffless blood pressure., Methods: A transmitter and receiver antenna are placed at the sternum to acquire the arterial pulsation at the aortic arch. A four-electrode arrangement across the shoulders acquires arterial pulse across the carotid and subclavian arteries from bioimpedance as well as a bipolar lead I electrocardiogram. The PTT and pulse arrival times (PATs) are measured on six healthy male subjects during exercise on a bicycle ergometer. Using linear regression, the estimated PAT and PTT values are calibrated to the systolic and mean as well as diastolic blood pressure from an oscillometric device., Results: For all subjects, the Pearson correlation coefficients for PAT-SBP and PTT-SBP are -0.66 (p = 0.001) and -0.48 (p = 0.0029), respectively. Correlation coefficients for individual subjects ranged from -0.54 to -0.9 and -0.37 to -0.95, respectively., Conclusion: The proposed system architecture is promising in estimating cuffless arterial blood pressure at the central, proximal arteries, which obey the Moens-Korteweg equation more closely when compared to peripheral arteries., Significance: An important advantage of PTT from the carotid and subclavian arteries is that the PTT over the central elastic arteries is measured instead of the peripheral arteries, which potentially reduces the changes in PTT due to vasomotion. Furthermore, the sensors can be completely hidden under a patients clothes, making them more acceptable by the patient for ambulatory monitoring.

Objective: Measurements of ultrawideband (UWB) pulses reflected by the human body are conducted to evidence the differences in the received signal time behaviors due to respiration phases, and to experimentally verify previously obtained numerical results on the body's organs responsible for pulse reflection., Methods: Two experimental setups are used. The first one is based on a commercially available impulse radar system integrated on a single chip, while the second one implements an indirect time-domain reflectometry technique using a vector network analyzer controlled by a LabVIEW virtual instrument running on a laptop., Results: When the UWB source is placed close to the human body, a small reflection due to the lung boundaries is present in the received pulse well distanced in time from the reflection due to the air-skin interface; this reflection proved to be linked to the different respiration phases., Conclusions: The changes in the reflected pulse could be used to detect, through wearable radar systems, lung movements associated with the breath activity., Significance: The development of a wearable radar system is of great importance because it allows the breath activity sensing without interfering with the subject daily activities.

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

In this paper, we propose a pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been validated with a Vicon motion capture system in the lab with 13 participants and 158 test runs. The study revealed that for an optimal step recognition and walking speed estimation, a dual radar set up with one radar placed at foot level and the other at torso level is necessary. An excellent absolute agreement with intraclass correlation coefficients of 0.97 was found for step time estimation with the foot level radar. For walking speed, although both radars show excellent consistency they all have a system offset compared to the ground truth due to walking direction with respect to the radar beam. The torso level radar has a better performance (9% offset on average) in the speed estimation compared to the foot level radar (13%-18% offset). Quantitative analysis has been performed to compute the angles causing the systematic error. These lab results demonstrate the capability of the system to be used as a daily gait assessment tool in home environments, useful for fall risk assessment and other health care applications. The system is currently being tested in an unstructured home environment.