In today's digital world, rapid technological advancements continue to lessen the burden of tasks for individuals. Among these tasks is communication across perceived language barriers. Indeed, increased attention has been drawn to American Sign Language (ASL) recognition in recent years. Camera-based and motion detection-based methods have been researched extensively; however, there remains a divide in communication between ASL users and non-users. Therefore, this research team proposes the use of a novel wireless sensor (Frequency-Modulated Continuous-Wave Radar) to help bridge the gap in communication. In short, this device sends out signals that detect the user's body positioning in space. These signals then reflect off the body and back to the sensor, developing thousands of cloud points per second, indicating where the body is positioned in space. These cloud points can then be examined for movement over multiple consecutive time frames using a cell division algorithm, ultimately showing how the body moves through space as it completes a single gesture or sentence. At the end of the project, 95% accuracy was achieved in one-object prediction as well as 80% accuracy on cross-object prediction with 30% other objects' data introduced on 19 commonly used gestures. There are 30 samples for each gesture per person from three persons.

Over half a million people in the United States use American Sign Language (ASL) as their primary mode of communication. Automatic ASL recognition would enable Deaf and Hard of Hearing (DHH) users to interact with others who are not familiar with ASL as well as voice-controlled digital assistants (e.g., Alexa, Siri, etc.). While ASL recognition has been extensively studied, there is a little attention given to recognition of ASL non-manual body markers. The non-manual markers are typically expressed through head, torso and shoulder movements, and add essential meaning and context to the signed sentences. In this work, we present ExASL, a sentence-level ASL recognition system using millimeter-wave radars. ExASL can recognize manual markers (hand gestures) and non-manual markers (head and torso movements). It utilizes multi-distance clustering to recognize body parts and cluster mmWave point clouds. We then present a multi-view deep learning algorithm that can learn from clustered body part representation for an expressive sentence-level recognition. Our evaluation shows that ExASL can recognize ASL sentences with a word error rate of 0.79%, sentence error rate of 1.25%, and non-manual markers with an accuracy of 83.5%.