Multi Antenna Radar System for American Sign Language (ASL) Recognition Using Deep Learning

TL;DR

This paper presents a radar-based system utilizing multi-antenna deep learning techniques for real-time American Sign Language recognition, capable of distinguishing signs from multiple individuals simultaneously.

Contribution

It introduces a novel RF-based approach combining joint spatio-temporal preprocessing and CNN classification for multi-person ASL recognition.

Findings

Effective separation of multiple signers using beamforming.

High accuracy in sign recognition demonstrated at 77 GHz.

Real-time processing capability achieved.

Abstract

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.