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Hidden Markov Model-based Gesture Recognition with FMCW Radar

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Citation Author(s):
Greg Malysa
Greg Malysa, Dan Wang, Lorin Netsch, Murtaza Ali
Submitted by:
Greg Malysa
Last updated:
6 December 2016 - 12:06pm
Document Type:
Presentation Slides
Document Year:
2016
Event:
GlobalSIP 2016
Presenters:
Greg Malysa
Paper Code:
1330
Categories:
Audio and Acoustic Signal Processing
Keywords:
Radar Signal Processing
 

In this paper we present experimental results for the development
of a gesture recognition system using a 77 GHz FMCW
radar system. We measure the micro-Doppler signature of a
gesturing hand to construct an energy distribution in velocity
space over time. A gesturing hand is fundamentally a dynamical
system with unobservable “state” (i.e. the name of the gesture)
which determines the sequence of associated observable
velocity-energy distributions, so a Hidden Markov Model is
used to for gesture recognition, a more tailored approach than
the SVM classifiers used in previous work. We also describe
a method for reducing the length of our feature vectors by a
factor of 12 without hurting the recognition performance, by
reparameterizing them in terms of a sum of Gaussians.

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