Identity authentication based on Doppler radar respiration
sensing is gaining attention as it requires neither contact
nor line of sight and does not give rise to privacy concerns
associated with video imaging. Prior research demonstrating the
recognition of individuals has been limited to isolated single subject
scenarios. When two equidistant subjects are present,
identification is more challenging due to the interference of
respiration motion patterns in the reflected radar signal. In this
research, respiratory signature separation techniques are functionally
combined with machine learning (ML) classifiers for
reliable subject identity authentication. An improved version of
the dynamic segmentation algorithm (peak search and triangulation)
was proposed, which can extract distinguishable airflow
profile-related features (exhale area, inhale area, inhale/exhale
speed, and breathing depth) for medium-scale experiments of
20 different participants to examine the feasibility of extraction
of an individual’s respiratory features from a combined mixture
of motions for subjects. Independent component analysis
with the joint approximation of diagonalization of eigenmatrices
(ICA-JADE) algorithm was employed to isolate individual respiratory
signatures from combined mixtures of breathing patterns.
The extracted hyperfeature sets were then evaluated by integrating
two different popular ML classifiers, k-nearest neighbor
(KNN) and support vector machine (SVM), for subject
authentication. Accuracies of 97.5% for two-subject experiments
and 98.33% for single-subject experiments were achieved, which
supersedes the performance of prior reported methods. The
proposed identity authentication approach has several potential
applications, including security/surveillance, the Internet-of-
Things (IoT) applications, virtual reality, and health monitoring.
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Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm
Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based non-contact physiological sensing technology. Prior research using Independent component analysis with the JADE algorithm has been limited to the separation of respiratory signatures for normal breathing patterns. This paper investigates the feasibility of using the ICA-JADE algorithm with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy for recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively.
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- Award ID(s):
- 1915738
- NSF-PAR ID:
- 10295833
- Date Published:
- Journal Name:
- 2020 IEEE Asia-Pacific Microwave Conference (APMC)
- Page Range / eLocation ID:
- 734 to 736
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Radar is an attractive approach for identity authentication because it requires no contact and is unobtrusive. Most reported results have focused only on sedentary breathing patterns, without considering how respiratory patterns may change due to physiological activities or emotional stress. In this research the feasibility of extracting identifying features from radar respiratory traces was tested, for sedentary subject conditions as well as just after performing physiological activities (walking upstairs). Respiratory breathing dynamics related features (breathing rate, spectral entropy, breathing depth, inhale/exhale area ratio, mean and standard deviation of the peaks) were extracted from radar captured respiration patterns, and variations in feature parameters after physiological activities were assessed. Experimental results demonstrated that, after short exertions dynamically segmented respiratory pattern exhale area and breathing depth increased by more than 1.4 times for all participants, which made evident the uniqueness of residual heart volume after expiration for recognizing each subject even after short exertions. Our proposed approach is also integrated with a Support Vector Machine (SVM) with a radial basis function kernel to demonstrate an identification success rate of almost 98.55% for sedentary-only conditions and almost 92% for a combined mixture of conditions (sedentary and after short exertion). While the efficacy was reduced, the method still shows significant potential. The proposed identity authentication approach has several potential applications including security/surveillance, IOT applications, virtual reality and health monitoring as well.more » « less
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null (Ed.)Unmanned Aerial Vehicles (UAVs) have demonstrated efficacy as a platform for remote life sensing in post-disaster search and rescue applications. Radar-assisted UAV respiration motion sensing technology also shows promise yet a significant technological challenge remains associated with interfering motion artefacts from the moving UAV platform. The feasibility of integrating an adaptive filter approach for the compensation of platform motion artefacts is investigated here for the extraction of respiratory motion signatures. A 24-GHz dual radar system was attached to a mechanical mover to emulating motion artefacts while measuring the motion of a robotic breathing phantom designed to reproduce breathing motion patterns. Recursive least square (RLS) and a least mean square (LMS) adaptive filter algorithms were employed to test efficacy for extracting respiratory rate from the motion corrupted breathing signal. Experimental results demonstrated that the RLS performed best with an accuracy of 98.24% for extracting the frequency of the robotic breathing phantom mover. The proposed system has several potential applications including military, humanitarian, and post-disaster search and rescue operations.more » « less
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Radar sensing of respiratory motion from unmanned aerial vehicles (UAVs) offers great promise for remote life sensing especially in post-disaster search and rescue applications. One major challenge for this technology is the management of motion artifacts from the moving UAV platform. Prior research has focused on using an adaptive filtering approach which requires installing a secondary radar module for capturing platform motion as a noise reference. This paper investigates the potential of the empirical mode decomposition (EMD) technique for the compensation of platform motion artifacts using only primary radar measurements. Experimental results demonstrated that the proposed EMD approach can extract the fundamental frequency of the breathing motion from the combined breathing and platform motion using only one radar, with an accuracy above 87%.more » « less
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Purpose To develop a 3D cones steady‐state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free‐breathing whole‐heart coronary magnetic resonance angiography.
Method The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k‐space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade‐off of an increased number of acquisition heartbeats. For verification, phyllotaxis‐cones is compared to sequential‐cones through simulations, phantom studies, and
in vivo coronary scans with 8 subjects using 2D image‐based navigators for retrospective motion correction.Results Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis‐cones compared to sequential‐cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis‐cones.
Conclusion Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free‐breathing whole‐heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady‐state free precession sequence.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/APMC47863.2020.9331715
