American Sign Language Recognition Using RF Sensing

TL;DR

This paper explores using RF sensors for non-invasive, privacy-preserving recognition of American Sign Language, demonstrating high differentiation between signers and non-signers and achieving 72.5% accuracy in classifying 20 signs.

Contribution

It introduces RF sensing as a novel, non-contact method for ASL recognition, addressing privacy and mobility issues of existing video and glove-based systems.

Findings

RF data can differentiate signing from daily activities.

Imitation signing differs from native ASL with 99% accuracy.

RF sensor fusion achieves 72.5% accuracy in classifying 20 signs.

Abstract

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.