American Sign Language Recognition Using an FMCW Wireless Sensor (Student Abstract)


  • Yuanqi Du George Mason University
  • Nguyen Dang George Mason University
  • Riley Wilkerson George Mason University
  • Parth Pathak George Mason University
  • Huzefa Rangwala George Mason University
  • Jana Kosecka George Mason University



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.




How to Cite

Du, Y., Dang, N., Wilkerson, R., Pathak, P., Rangwala, H., & Kosecka, J. (2020). American Sign Language Recognition Using an FMCW Wireless Sensor (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13781-13782.



Student Abstract Track