Continuous-wave Doppler Radar Sensor Research Articles

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Effective fall detection and post-fall breath rate tracking using a low-cost CW Doppler radar sensor

Existing low-cost Doppler radar-based fall detection systems encounter challenges due to false alarms and the absence of post-fall health tracking, significantly impacting their accuracy and overall compatibility for fall detection. This paper presents a cost-effective, robust solution for a fall detection system with the post-fall health tracking facility using a 3.18 GHz continuous-wave Doppler radar sensor. The experimental data acquisition is conducted in-house under the guidance of a healthcare expert, involving various activities such as standing, sitting, sleeping, running, walking, falling, sit-to-stand, and stand-to-sit transitions. We propose an algorithm comprising four hierarchical stages, each with specific objectives. Considering the complexity, the model is trained differently for each stage to optimize the classification accuracy. The system architecture is designed to minimize computational costs and power consumption through modular implementation in stages, utilizing low-power equipment and incorporating traditional machine-learning algorithms. Experimental results demonstrate a fall detection accuracy of 93.24% and breath rate measurement error of 2.26%, which is competitive with recent state-of-the-art approaches. Obtained results highlight the effectiveness of the proposed system in addressing the challenges of false alarms and post-fall health tracking while maintaining cost-efficiency and accuracy in fall detection.

High Accuracy Heartbeat Detection from CW-Doppler Radar Using Singular Value Decomposition and Matched Filter.

Heart rate measurement using a continuous wave Doppler radar sensor (CW-DRS) has been applied to cases where non-contact detection is required, such as the monitoring of vital signs in home healthcare. However, as a CW-DRS measures the speed of movement of the chest surface, which comprises cardiac and respiratory signals by body motion, extracting cardiac information from the superimposed signal is difficult. Therefore, it is challenging to extract cardiac information from superimposed signals. Herein, we propose a novel method based on a matched filter to solve this problem. The method comprises two processes: adaptive generation of a template via singular value decomposition of a trajectory matrix formed from the measurement signals, and reconstruction by convolution of the generated template and measurement signals. The method is validated using a dataset obtained in two different experiments, i.e., experiments involving supine and seated subject postures. Absolute errors in heart rate and standard deviation of heartbeat interval with references were calculated as and for the lying posture, and and for the sitting posture.

A Movement Detection System Using Continuous-Wave Doppler Radar Sensor and Convolutional Neural Network to Detect Cough and Other Gestures.

The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.

Fast heart rate extraction using CW Doppler radar with interpolated discrete Fourier transform algorithm

Doppler radar-based fast heart rate (HR) extraction has great application potential in stress and emotion recognition, anxiety treatments, etc. However, fast extraction of HR is still a great challenge for the vital sign detection applying the traditional discrete Fourier transform (DFT) method based on a continuous-wave (CW) Doppler radar sensor. When applying traditional DFT to the baseband signal analysis, the spectrum resolution will become insufficient if the time window is less than 10 s. In this paper, an interpolated DFT algorithm based on a poly-item cosine window is introduced to achieve fast extraction of HR with only 3 s data length. In addition, the leakage and grid effect phenomenon is presented to show the defects caused by the traditional DFT method. To verify the effectiveness of the proposed method, simulations are performed and experiments are executed using a 10-GHz CW Doppler radar sensor platform. Compared with the traditional DFT method, the interpolated DFT method reduces the average HR error from 8.48% to 1.87% based on the Hanning window and from 8.48% to 1.45% based on the rectangular window.

Two‐tone continuous‐wave Doppler radar based on envelope detection method

AbstractA continuous‐wave (CW) Doppler radar using two‐tone frequencies is presented for the measurement of short‐range distance. The envelope detection method is proposed to discriminate each frequency signal in the output waveform of the two‐tone CW Doppler radar sensor. The phase difference in each frequency is obtained from the nonlinearly demodulated I/Q baseband signals, following calibration of the I/Q imbalance and DC offset voltages. The absolute distance is derived from the phase differences at the two‐tone frequencies with a 100 MHz spacing. The measurement results in the two‐tone radar system show a maximum error of 2.5 cm in the 20 to 50 cm distance range.

Speed Calibration and Traceability for Train-Borne 24 GHz Continuous-Wave Doppler Radar Sensor

The 24 GHz continuous-wave (CW) Doppler radar sensor (DRS) is widely used for measuring the instantaneous speed of moving objects by using a non-contact approach, and has begun to be used in train-borne movable speed measurements in recent years in China because of its advanced performance. The architecture and working principle of train-borne DRSs with different structures including single-channel DRSs used for freight train speed measurements in railway freight dedicated lines and dual-channel DRSs used for speed measurements of high-speed and urban rail trains in railway passenger dedicated lines, are first introduced. Then, the disadvantages of two traditional speed calibration methods for train-borne DRS are described, and a new speed calibration method based on the Doppler shift signal simulation by imposing a signal modulation on the incident CW microwave signal is proposed. A 24 GHz CW radar target simulation system for a train-borne DRS was specifically realized to verify the proposed speed calibration method for a train-borne DRS, and traceability and performance evaluation on simulated speed were taken into account. The simulated speed range of the simulation system was up to (5~500) km/h when the simulated incident angle range was within the range of (45 ± 8)°, and the maximum permissible error (MPE) of the simulated speed was ±0.05 km/h. Finally, the calibration and uncertainty evaluation results of two typical train-borne dual-channel DRS samples validated the effectiveness and feasibility of the proposed speed calibration approach for a train-borne DRS with full range in the laboratory as well as in the field.

Correction: Park, J.-H. et al. 915-MHz Continuous-Wave Doppler Radar Sensor for Detection of Vital Signs. Electronics 2019, 8, 561

The authors wish to make the following corrections to the published paper [...]

915-MHz Continuous-Wave Doppler Radar Sensor for Detection of Vital Signs

A miniaturized continuous-wave Doppler radar sensor operating at 915 MHz to remotely detect both respiration and heart rate (beats per minute) is presented. The proposed radar sensor comprises a front-end module including an implemented complementary metal-oxide semiconductor low-noise amplifier (LNA) and fractal-slot patch antennas, whose area was reduced by 15.2%. The two-stage inverter-based LNA was designed with an interstage capacitor and a feedback resistor to acquire ultrawide bandwidth. Two operating frequencies, 915 MHz and 2.45 GHz, were analyzed with regard to path loss for efficient operation because frequency affects detection sensitivity, reflected signal power from the human body, and measurement distance in a far-field condition. Path-loss calculation based on the simplified layer model indicates that the reflected power of the 915 MHz radar could be higher than that of the 2.45 GHz radar. The implemented radar front-end module excluding the LNA occupies 35 × 55 mm2. Vital signs were obtained via a fast Fourier transform and digital filtering using raw signals. In an experiment with six subjects, the respiration and heart rate obtained at 0.8 m using the proposed radar sensor exhibited mean accuracies of 99.4% and 97.6% with respect to commercialized reference sensors, respectively.

Remote Blind Motion Separation Using a Single-Tone SIMO Doppler Radar Sensor

It is a challenging task for a continuous-wave Doppler radar sensor (DRS) to linearly separate independent motions without any prior information. In this paper, a low-IF single-input multiple-output (SIMO) DRS system is designed and implemented for motion separation. The SIMO DRS system detects the signals scattered off multiple objects, and a two-step blind motion separation approach is proposed to separate motions from the DRS output. Experiments show that the SIMO DRS and the proposed motion separation approach can successfully separate combinations of triangular, sinusoidal, and random motions, as long as the velocities or initial phases of such motions are different. With the two-step approach sequentially tackling the signal separation and the nonlinear demodulation problems, both small- and large-scale independent motions can be linearly separated. These results imply the potential in practical applications of short-range tracking and localization of moving objects.

Indoor Localization for Passive Moving Objects Based on a Redundant SIMO Radar Sensor

In the past decade, many efforts have been devoted to indoor localization solutions. While significant progresses have been achieved, short-range localization of passive moving objects in indoor environments remains a technical challenge, especially when available radio spectrum is limited. In this paper, we resolve this challenge by proposing a single-frequency continuous-wave Doppler radar sensor implemented with a redundant single-input multiple-output architecture. Since only Doppler phase shift information is used in the proposed tracking-localization solution, the localization is naturally immune to stationary clutters such as walls and furniture, making this indoor localization only responsive to moving objects. Only using a single frequency is required, such an indoor localization can be implemented with the highest spectrum and energy efficiency. The obtained results imply the potential for the detection of human activities and vital signs in indoor environments with strong stationary scatters.

Basic feasibility of breathing and pulse detection with a continuous wave Doppler radar sensor

s / Resuscitation 96S (2015) 43–157 53 not better in comparison with other causes of CA. These data suggest that further efforts are needed to prevent intubation-related CA. http://dx.doi.org/10.1016/j.resuscitation.2015.09.122