  This paper investigates RF-based system for automatic American Sign Language
(ASL) recognition. We consider radar for ASL by joint spatio-temporal
preprocessing of radar returns using time frequency (TF) analysis and
high-resolution receive beamforming. The additional degrees of freedom offered
by joint temporal and spatial processing using a multiple antenna sensor can
help to recognize ASL conversation between two or more individuals. This is
performed by applying beamforming to collect spatial images in an attempt to
resolve individuals communicating at the same time through hand and arm
movements. The spatio-temporal images are fused and classified by a
convolutional neural network (CNN) which is capable of discerning signs
performed by different individuals even when the beamformer is unable to
separate the respective signs completely. The focus group comprises individuals
with varying expertise with sign language, and real time measurements at 77 GHz
frequency are performed using Texas Instruments (TI) cascade radar.
