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.