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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Respiratory Feature Extraction for Radar-Based Continuous Identity Authentication
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.
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- Award ID(s):
- 1915738
- NSF-PAR ID:
- 10295836
- Date Published:
- Journal Name:
- 2020 IEEE Radio and Wireless Symposium (RWS)
- Page Range / eLocation ID:
- 119 to 122
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RWS45077.2020.9050013
