Medical Sensing Using Doppler Radar

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

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A 2.4 GHz Compact Doppler Radar Module for Vibration Monitoring

2019 IEEE MTT-S International Microwave and RF Conference (IMARC) ◽

10.1109/imarc45935.2019.9118746 ◽

2019 ◽

Author(s):

Pallab Kumar Gogoi ◽

Mrinal Kanti Mandal ◽

Ayush Kumar ◽

Tapas Chakravarty

Keyword(s):

Doppler Radar ◽

Vibration Monitoring

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

Sensors ◽

10.3390/s21113588 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3588

Author(s):

Yuki Iwata ◽

Han Trong Thanh ◽

Guanghao Sun ◽

Koichiro Ishibashi

Keyword(s):

Heart Rate ◽

Singular Value Decomposition ◽

Continuous Wave ◽

Doppler Radar ◽

Matched Filter ◽

Singular Value ◽

Home Healthcare ◽

Body Motion ◽

Sitting Posture ◽

Value Decomposition

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 1.93±1.76bpm and 57.0±28.1s for the lying posture, and 9.72±7.86bpm and 81.3±24.3s for the sitting posture.

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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽

10.3390/s21113937 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3937

Author(s):

Seungeon Song ◽

Bongseok Kim ◽

Sangdong Kim ◽

Jonghun Lee

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Smart Home ◽

Doppler Radar ◽

Radar Images ◽

Radar Signature ◽

Memory Efficiency ◽

High Compression ◽

Value Decomposition ◽

Deep Learning Model

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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