A Review on Radar-Based Human Detection Techniques.

Radar systems are diverse and used in industries such as air traffic control, weather monitoring, and military and maritime applications. Within the scope of this study, we focus on using radar for human detection and recognition. This study evaluated the general state of micro-Doppler radar-based human recognition technology, the related literature, and state-of-the-art methods. This study aims to provide guidelines for new research in this area. This comprehensive study provides researchers with a thorough review of the existing literature. It gives a taxonomy of the literature and classifies the existing literature by the radar types used, the focus of the research, targeted use cases, and the security concerns raised by the authors. This paper serves as a repository for numerous studies that have been listed, critically evaluated, and systematically classified.

Convolutional Neural Network-Based Drone Detection and Classification Using Overlaid Frequency-Modulated Continuous-Wave (FMCW) Range-Doppler Images.

This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range-Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range-Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods.

Validation of a micro-doppler radar for measuring gait modifications during multidirectional visual perturbations.

BACKGROUND: Changes in spatio-temporal gait parameters and their variability during balance-challenging tasks are markers of motor performance linked to fall risk. Radio frequency (RF) sensors hold great promise towards achieving continuous remote monitoring of these parameters. RESEARCH QUESTIONS: To establish the concurrent validity of RF-based gait metrics extracted using micro-Doppler (µD) signatures and to determine whether these metrics are sensitive to gait modifications created by multidirectional visual perturbations. METHODS: Fifteen participants walked overground in a virtual environment (VE) and VE with medio-lateral (ML) and antero-posterior (AP) perturbations. An optoelectronic motion capture system and one RF sensor were used to extract the linear velocity of the trunk and estimate step time (ST), step velocity (SV), step length (SL), and their variability (STV, SVV, and SLV). Intra-class coefficient for consistency (ICC), mean and standard deviation of the differences (MD), 95 % limits of agreement, and Pearson correlation coefficients (r) were used to determine concurrent validity. One-way repeated-measures analysis of variance was used to analyze the main and interaction effects of visual conditions. RESULTS: All outcomes showed good to excellent reliability (r>0.795, ICC>0.886). Average gait parameters showed good to excellent agreement, with values obtained with the RF sensor systematically smaller than the values obtained with the markers (MD of 0.001 s, 0.09 m/s, and 0.06 m). Gait variability parameters showed poor to moderate agreement, with values obtained with the RF sensor systematically larger than those obtained with the markers (MD of 1.9 %-3.9 %). Both measurement systems reported decreased SL and SV during ML perturbations, but the gait variability parameters extracted with the radar were not able to detect the higher STV and SLV during this condition. SIGNIFICANCE: The radar µD signature is a valid and reliable method for the assessment of average spatio-temporal gait parameters but gait variability measures need to be viewed with caution because of the lower levels of agreement and sensitivity to ML visual perturbations. This work represents an initial investigation for the development of a low-cost system that will facilitate aging-in-place by providing remote monitoring of gait in natural settings.

Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment.

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

FMCW Radar Human Action Recognition Based on Asymmetric Convolutional Residual Blocks.

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.

A New Method for Traffic Participant Recognition Using Doppler Radar Signature and Convolutional Neural Networks.

The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time-frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-µRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Preoperative Angiographic Protocol for the Surgical Management of Spinal Dural Arteriovenous Fistulas: A Technical Nuance for Safe Minimally Invasive Surgery.

BACKGROUND: Interruption of the fistulous point is the goal of treatment of spinal dural arteriovenous fistulas (dAVFs). Microsurgery remains a highly efficient treatment in terms of complete occlusion with the lowest risk of recurrence rate. It is reported that the hardest step involves finding the fistulous site itself, potentially extending surgical access and time and increasing potential postoperative surgical-related complications. The accurate preoperative detection of the shunt and spinal level together is crucial for guiding optimal, fast, and safe microsurgical treatment. METHODS: We describe a preoperative angiographic protocol for achieving a safe and simple resection of spinal dural arteriovenous fistulas based on a 6-year institutional experience of 42 patients who underwent minimally invasive procedures. Two illustrative cases are included to support the technical descriptions. RESULTS: The suspected artery associated with the vascular malformation of interest is studied in our angiographic protocol through nonsubtracted selective acquisitions in lateral projection. The resulting frames are reconstructed with three-dimensional rotational angiography. The implementation of the preoperative angiographic protocol allowed 100% of intraoperative identification of the fistulous point in all cases with the use of a minimally invasive approach. CONCLUSIONS: Nowadays, neurosurgeons advocate for minimally invasive procedures and procedures with low morbidity risk for treatment of spinal dural arteriovenous fistulas. Our preoperative approach for accurate angiographic localization of the fistulous point through nonsubtracted and three-dimensional reconstructed angiography allowed us to achieve safe and definitive occlusion of the shunt.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar.

Micro-Doppler time-frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time-frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time-frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time-frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging.

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. In this study, we present a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. To evaluate its performance, we conducted experiments using a dataset of 51 rat brain scans. The training positions were extracted at intervals of 375 µm, resulting in a positioning error of 176 µm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method's reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but rather on conserved vascular patterns.

Remote Emotion Recognition Using Continuous-Wave Bio-Radar System.

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions.

Radar Micro-Doppler Signature Generation Based on Time-Domain Digital Coding Metasurface.

Micro-Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro-Doppler database poses a great challenge, since plenty of experiments are required to get the micro-Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time-consuming. Aiming to overcome these limits, a low-cost and powerful simulation platform of the micro-Doppler effects is proposed based on time-domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave-matter interactions, it enables to supply rich and high-precision electromagnetic signals with multiple micro-Doppler frequencies to describe the micro-motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing.

Micro-motion characteristics of multi-feature targets on the double pulse coherent system.

In this paper, we study the micro-motion characteristics of multi-feature targets based on a double pulse coherent system under atmospheric conditions. The theoretical model for echo signal and micro-motion characteristics of a 3D target in double pulse coherent system is deduced. We discuss the influence of micro-motion characteristics, the relative size of light spot and target, target shapes, and incident direction on frequency shift. LRCS (Lidar cross-section), echo waveform, intensity and radiation energy distribution under different conditions are obtained additionally. Simulation results conclude that these parameters are of advantage to the inversion of target shape properties and motion types.

Comparison of IMD and ICG videoangiography in combined bypass surgery: a single-center study.

BACKGROUND: Superficial temporal artery-middle cerebral artery (STA-MCA) bypass combined with an encephaloduromyosynangiosis (EDMS) had gained significant role in treating chronic cerebral ischemia. Invasiveness and costs of intraoperative digital subtraction angiography (DSA) limited its application in operations. OBJECTIVE: To find the reliable parameters for determining bypass patency with intraoperative micro-Doppler (IMD) sonography and compare the diagnostic accuracy of indocyanine green (ICG) videoangiography with IMD in combined bypass. METHOD: One hundred fifty bypass procedures were included and divided into patent and non-patent groups according to postoperative computed tomography angiography (CTA) within 72 h. The surgical process was divided into four phases in the following order: preparation phase (phase 1), anastomosis phase (phase 2), the temporalis muscle closure phase (phase 3), and the bone flap closure phase (phase 4). The IMD parameters were compared between patent and non-patent groups, and then compared with the patency on CTA by statistical analyses. IMD with CTA, ICG videoangiography with CTA, IMD with ICG videoangiography were performed to assess bypass patency. The agreement between methods was evaluated using kappa statistics. RESULTS: No significant differences of baseline characteristics were found between patent and non-patent group. Parameters in the STA were different between patent and non-patent groups in phases 2, 3, and 4. In patent group, Vm was apparently higher and PI was lower in phases 2, 3, and 4 compared with phase 1 (P < .001). In non-patent group, no differences of Vm and PI were found within inter-group. The best cutoff value of IMD in the STA to distinguish patent from non-patent bypasses was Vm in phase 4 > 17.5 cm/s (sensitivity 94.2%, specificity 100%). In addition, the agreement for accessing bypass patency was moderate between ICG videoangiography and CTA (kappa = 0.67), IMD and ICG videoangiography (kappa = 0.73), and good between IMD and CTA (kappa = 0.86). CONCLUSION: ICG videoangiography could directly display morphology changes of bypass. IMD could be used for providing half-quantitative parameters to assess bypass patency. Vm in phase 4 > 17.5 cm/s suggesting the patency of bypass on CTA would be good. Also, compared with ICG videoangiography, IMD had more accuracy.

Hand Gesture Recognition Using FSK Radar Sensors.

Hand gesture recognition, which is one of the fields of human-computer interaction (HCI) research, extracts the user's pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range.

Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning.

Accurately classifying and identifying non-cooperative targets is paramount for modern space missions. This paper proposes an efficient method for classifying and recognizing non-cooperative targets using deep learning, based on the principles of the micro-Doppler effect and laser coherence detection. The theoretical simulations and experimental verification demonstrate that the accuracy of target classification for different targets can reach 100% after just one round of training. Furthermore, after 10 rounds of training, the accuracy of target recognition for different attitude angles can stabilize at 100%.

Decibel Decisions: The Concept of Intracranial Aneurysm Surgery With a Decibel Meter on Two Surgical Cases.

The present cases were used to investigate the reliability of the intraoperative decibel meter as an objective method of clipping efficiency in cerebral aneurysm surgery and to assess the impact of this method on the surgical procedure itself. Different methodologies have been developed and applied to directly or indirectly evaluate the placement of a clip, for example, intraoperative digital subtraction angiography (DSA), intraoperative micro-Doppler ultrasonography, and, more recently, indocyanine green (ICG). We included two patients with a previously non-treated unruptured brain aneurysm. In both patients, intraoperative micro-Doppler was used in combination with a decibel meter app. Here, we present the cases of two patients. In patient one, the pre-clipping average sound level/equivalent continuous sound pressure level (Avg/Leq) was 96.7 dB, while the post-clipping Avg/Leq was 94.4 dB, indicating a reduction in sound level after clipping. Similarly, the pre-clipping time-weighted average noise level (TWA) was 1.2%, while the post-clipping TWA was 0.5%, indicating a decrease in exposure dose after clipping. In patient two, the average sound level for the post-clipping measurement (94.2 dB) was higher than the pre-clipping measurement (93.5 dB), but the difference was not statistically significant. These cases indicate the potential for using sound measurements as a reliable indicator of adequate aneurysm occlusion during clipping procedures. Further research is needed to confirm these findings.

Micro-Doppler Effect and Sparse Representation Analysis of Underwater Targets.

At present, the micro-Doppler effects of underwater targets is a challenging new research problem. This paper studies the micro-Doppler effect of underwater targets, analyzes the moving characteristics of underwater micro-motion components, establishes echo models of harmonic vibration points and plane and rotating propellers, and reveals the complex modulation laws of the micro-Doppler effect. In addition, since an echo is a multi-component signal superposed by multiple modulated signals, this paper provides a sparse reconstruction method combined with time-frequency distributions and realizes signal separation and time-frequency analysis. A MicroDopplerlet time-frequency atomic dictionary, matching the complex modulated form of echoes, is designed, which effectively realizes the concise representation of echoes and a micro-Doppler effect analysis. Meanwhile, the needed micro-motion parameter information for underwater signal detection and recognition is extracted.

Micro-Doppler for venous sinus localization in approaches to the cerebello-pontine angle.

BACKGROUND: Main anatomical landmarks of retrosigmoid craniotomy are transverse sinus (TS), sigmoid sinus (SS), and the confluence of both. Anatomical references and guidance based on preoperative imaging studies are less reliable in the posterior fossa than in the supratentorial region. Simple intraoperative real-time guidance methods are in demand to increase safety. METHODS: This manuscript describes the localization of TS, SS, and TS-SS junction by audio blood flow detection with a micro-Doppler system. CONCLUSION: This is an additional technique to increase safety during craniotomy and dura opening, widening the surgical corridor to secure margins without carrying risks nor increase surgical time.