Abstract: A system includes a frequency modulated continuous wave (FMCW) transceiver, a processor, and a memory. The memory stores program instructions that, when executed by the processor, cause the system to receive a signal representative of an FMCW signal reflected from an object of interest, apply a first Fourier transform to the signal to obtain range data, identify a subset of the range data corresponding to a region of interest, apply a second Fourier transform on the identified subset of the range data to obtain velocity data corresponding to the identified subset of the range data, and identify a gesture performed by the object of interest based on at least a portion of velocity data.

Abstract: A system includes a frequency modulated continuous wave (FMCW) transceiver, a processor, and a memory. The memory stores program instructions that, when executed by the processor, cause the system to receive a signal representative of an FMCW signal reflected from an object of interest, apply a first Fourier transform to the signal to obtain range data, identify a subset of the range data corresponding to a region of interest, apply a second Fourier transform on the identified subset of the range data to obtain velocity data corresponding to the identified subset of the range data, and identify a gesture performed by the object of interest based on at least a portion of velocity data.

Abstract: A gesture recognition system is shown using a 77 GHz FMCW radar system. The signature of a gesturing hand is measured to construct an energy distribution in velocity space over time. A gesturing hand is fundamentally a dynamical system with unobservable “state” (i.e. the type of the gesture) which determines the sequence of associated observable velocity-energy distributions, therefore a Hidden Markov Model is used to for gesture recognition. A method for reducing the length of the feature vectors by a factor of 12 is also shown, by re-parameterizing the feature vectors in terms of a sum of Gaussians without decreasing the recognition performance.

Abstract: A gesture recognition system is shown using a 77 GHz FMCW radar system. The signature of a gesturing hand is measured to construct an energy distribution in velocity space over time. A gesturing hand is fundamentally a dynamical system with unobservable “state” (i.e. the type of the gesture) which determines the sequence of associated observable velocity-energy distributions, therefore a Hidden Markov Model is used to for gesture recognition. A method for reducing the length of the feature vectors by a factor of 12 is also shown, by re-parameterizing the feature vectors in terms of a sum of Gaussians without decreasing the recognition performance.