  Many technologies for human-computer interaction have been designed for
hearing individuals and depend upon vocalized speech, precluding users of
American Sign Language (ASL) in the Deaf community from benefiting from these
advancements. While great strides have been made in ASL recognition with video
or wearable gloves, the use of video in homes has raised privacy concerns,
while wearable gloves severely restrict movement and infringe on daily life.
Methods: This paper proposes the use of RF sensors for HCI applications serving
the Deaf community. A multi-frequency RF sensor network is used to acquire
non-invasive, non-contact measurements of ASL signing irrespective of lighting
conditions. The unique patterns of motion present in the RF data due to the
micro-Doppler effect are revealed using time-frequency analysis with the
Short-Time Fourier Transform. Linguistic properties of RF ASL data are
investigated using machine learning (ML). Results: The information content,
measured by fractal complexity, of ASL signing is shown to be greater than that
of other upper body activities encountered in daily living. This can be used to
differentiate daily activities from signing, while features from RF data show
that imitation signing by non-signers is 99\% differentiable from native ASL
signing. Feature-level fusion of RF sensor network data is used to achieve
72.5\% accuracy in classification of 20 native ASL signs. Implications: RF
sensing can be used to study dynamic linguistic properties of ASL and design
Deaf-centric smart environments for non-invasive, remote recognition of ASL. ML
algorithms should be benchmarked on native, not imitation, ASL data.
