IEEE - Aerospace and Electronic Systems - December 2021 - 20

BreathID: Radar's New Role in Biometrics
Figure 3.
Unique feature extraction. Measurement setup with transmission
(Tx) and receiving (Rx) antennas shown for a radar transceiver
with a test participant seated on a chair (a). After capturing the
respiration pattern, different respiratory related dynamic features
were extracted for 20 participants. In this feature list inhale rate,
exhale rate, heart rate, inhale area, exhale area, inhale/exhale area
ratio, and breathing depth were extracted. From [12].
that segment and to calculate area ratios. Figure 4(b) illustrates
that after performing physiological activities, subject
exhale areas are significantly different for each
participant. As different people intake different amounts
of air there is significant variation in their airflow profile.
After collecting all these respiratory dynamics-related features,
they were put as a matrix into the ML classifier for
recognizing different participants. It should be mentioned
that in our experiment we considered both normal breathing
dynamics and breathing dynamics captured after performing
brief exertions. Fig. 4(c) illustrates that the
confusion matrix where the diagonal column represents
the success rate of recognizing people. From Fig. 4(c), the
results for three participants have a success rate for the
combined mixtures of around 90%, and just for sedentary
breathing alone, it was around 95%. The classification
accuracy indicates that the proposed system has potential
benefits for biometric identification systems.
Other research groups also demonstrated the efficacy
of this technology, with some using heart-based dynamics,
like a cardiac cycle, for different participants. After capturing
radar-respiration signals they took five different
points within the segment of inhaling and exhale [6], [9].
In between five different points, they calculated the time
and area that acts as a unique feature. Using these heart20
based
dynamic features they also integrated an ML classifier
to recognize people from their heart-based geometry.
Over the last five years, there is a significant improvement
in terms of accuracy of recognizing people from their
breathing dynamics [11]. In our most recent reported
research, we also proposed a new improved version of the
dynamic segmentation algorithm to improve accuracy and
our reported results are still under review [16], [17]. In the
improved version of the dynamic segmentation algorithm,
we integrated both the heart and respiratory-based dynamics
by introducing peak search and triangulation methods
together. Previously reported dynamic segmentation algorithm
considers the 30%-70% amplitude of the signal so it
excluded the heart-beat-related information as heartbeat
information lies in between the inhale and exhale transition
[10], [16]. For the newly proposed algorithm, we
have created an array of maxima and minima of the signal
and then constructed a triangle between three points (two
consecutive maxima and one minimum) and calculated
the area of the triangle for inhaling and exhale episodes.
Then we extracted the average of inhaling and exhaling
areas within a window of 12 seconds [16]. We selected
a 12 seconds window based on the FFT window size
sampling limit and within a particular window, there
should be multiple complete breathing cycles [16], [17].
Integrating the peaks helps to consider the effect of
cardiac dynamics, where the inhale area illustrates ventricular
filling and the exhale area represents ventricular
contraction. Figure 5 illustrates the inhale and exhale area
differences within radar captured respiration patterns for
six different participants. This improved version of the
algorithm significantly increased the accuracy of the
system both for sedentary and postphysiological activity
breathing [16]. Continued experimentation and further
exploration are required to bring this sensor technology
into the practical real-world ofimplementation.
CHALLENGES OF NON-CONTACT IDENTITY
AUTHENTICATION TECHNOLOGY
Advancements in artificial intelligence and ML algorithms
enable radar non-contact identity authentication technology
to extract and analyze relevant identity features,
including the biometric characteristic of the cardiac signal
[11]. However, there remain significant challenges that
need to be overcome to bring this technology into practice.
One of the challenges researchers are investigating is the
implementation of this sensor technology in multisubject
environments [18]. Because in real life, especially in airports
or in-home environments, there is a probability of
the presence of multiple subjects in front of the radar.
When there are multiple subjects present, a combined
mixture of breathing patterns results, from which it is
difficult to separate individual respiration patterns.
IEEE A&E SYSTEMS MAGAZINE
DECEMBER 2021
IEEE - Aerospace and Electronic Systems - December 2021

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