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\title{Human Detection using Ultrasonic Doppler Vibrometry}

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\author{\underline{Asif Mehmood}$^{1}$ , Paul M. Goggans$^{1}$,
James M. Sabatier$^{2}$ \\ and Alexander E. Ekimov$^{2}$
\\
          The University of Mississippi\\
       (1) Electrical Engineering Department\\
         (2) National Center for Physical Acoustics\\
       University, MS 38677 \\
        (e-mail: amehmood@olemiss.edu)
       }

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\begin{abstract}
\noindent This paper considers the problem of distinguishing a
walking human from other moving or stationary objects in near
real-time using Bayesian model comparison and data obtained from
an ultrasonic Doppler vibrometer (UDV). Here we describe our
initial experimental and analytical work to develop an automated
non-invasive model-based approach for recognizing people based on
their measured velocity signal while walking. Our experimental set
up uses an ultrasonic Doppler vibrometer  as a non-contact means
for obtaining data related to the velocity of the moving body
components. The main advantages of using an ultrasonic vibrational
measurement system is high resolution, low cost, and ease of
installation. In the UDV an ultrasonic transducer directs a 50 kHz
acoustic wave to the moving body surface. The returned acoustic
signal, frequency modulated by the velocity of the body
components, is received by a co-located transducer whose output is
sampled to produce the output data time series.  Our experiment is
laboratory based and intended to determine basic capabilities. The
presence of a characteristic and approximately sinusoidal back
motion component in the observed velocity of a walking human is
used as a basis for distinguishing a walking human from other
moving objects. We have two models, one representing data
attributed to human bulk velocity and back velocity and the other
model representing a constant (possibly zero) bulk velocity. In
the walking human model, the back motion can be linked to whole
body motion. For the detection of walking human, we make use of
the Bayesian inference approach, where models are compared by
computing their posterior odds ratio. In this paper we present
parameters estimation results for the walking human model and
discuss our initial model selection results.




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