scholarly journals Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2351 ◽  
Author(s):  
Nebojša Malešević ◽  
Vladimir Petrović ◽  
Minja Belić ◽  
Christian Antfolk ◽  
Veljko Mihajlović ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Vital Signs ◽  
Ground Truth ◽  
Mental States ◽  
Radar Signal ◽  
Heart Activity ◽  
Continuous Wave Doppler ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

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

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 855 ◽  
Author(s):  
Park ◽  
Jeong ◽  
Lee ◽  
Oh ◽  
Yang
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Vital Signs ◽  
Radar Sensor ◽  
Continuous Wave Doppler ◽  
Published Paper

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

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

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 561 ◽  
Author(s):  
Jae-Hyun Park ◽  
Yeo-Jin Jeong ◽  
Ga-Eun Lee ◽  
Jun-Taek Oh ◽  
Jong-Ryul Yang
Keyword(s):  
Heart Rate ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Vital Signs ◽  
Path Loss ◽  
Low Noise ◽  
Radar Sensor ◽  
Continuous Wave Doppler ◽  
Front End ◽  
Front End Module

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.

Continuous-Wave Doppler Radar for Human Gait Classification

2017 ◽  
pp. 95-119
Author(s):  
Fok Hing Chi Tivive ◽  
Abdesselam Bouzerdoum ◽  
Bijan G. Mobasseri
Keyword(s):  
Continuous Wave ◽  
Doppler Radar ◽  
Human Gait ◽  
Continuous Wave Doppler ◽  
Gait Classification

scholarly journals FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6443
Author(s):  
Jinmoo Heo ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Signal Processing ◽  
Detection Rate ◽  
Continuous Wave ◽  
Vital Signs ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Fmcw Radar ◽  
Design And Implementation ◽  
Fft Processor ◽  
Point Operator

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

scholarly journals Neural Networks Application for Processing of the Data from the FMICW Radars

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1308 ◽  
Author(s):  
Lubos Rejfek ◽  
Tan N. Nguyen ◽  
Pavel Chmelar ◽  
Ladislav Beran ◽  
Phuong T. Tran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Processing ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Target Area ◽  
The Neural Network ◽  
Machine Learning Applications

In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

scholarly journals A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

2019 ◽  
Vol 11 (10) ◽  
pp. 1237 ◽  
Author(s):  
Hyunjae Lee ◽  
Byung-Hyun Kim ◽  
Jin-Kwan Park ◽  
Jong-Gwan Yook
Keyword(s):  
Vital Sign ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Vital Signs ◽  
Phase Information ◽  
Multiple Targets ◽  
Wave Radar ◽  
24 Ghz

A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.

Sign in / Sign up

Export Citation Format

Share Document