  The past decade has seen great advancements in speech recognition for control
of interactive devices, personal assistants, and computer interfaces. However,
Deaf and hard-ofhearing (HoH) individuals, whose primary mode of communication
is sign language, cannot use voice-controlled interfaces. Although there has
been significant work in video-based sign language recognition, video is not
effective in the dark and has raised privacy concerns in the Deaf community
when used in the context of human ambient intelligence. RF sensors have been
recently proposed as a new modality that can be effective under the
circumstances where video is not. This paper considers the problem of
recognizing a trigger sign (wake word) in the context of daily living, where
gross motor activities are interwoven with signing sequences. The proposed
approach exploits multiple RF data domain representations (time-frequency,
range-Doppler, and range-angle) for sequential classification of mixed motion
data streams. The recognition accuracy of signs with varying kinematic
properties is compared and used to make recommendations on appropriate trigger
sign selection for RFsensor based user interfaces. The proposed approach
achieves a trigger sign detection rate of 98.9% and a classification accuracy
of 92% for 15 ASL words and 3 gross motor activities.
