(Georgia Institute of Technology, 2022-05)
Agrawal, Pranay
95% of deaf children are born to hearing parents and lack continuous exposure to language, which often inhibits learning. We are developing Adaptive CopyCat, an educational game where Deaf children communicate with the computer via American Sign Language (ASL) in order to improve their language skills and working memory. While previous versions of CopyCat relied on custom hardware such as colored gloves with accelerometers for sign verification, our current version of the game utilizes off-the-shelf 4K RGB depth cameras and pose estimators. Before re-creating the game for Deaf children, we evaluate the efficacy of our current CopyCat ASL recognition system with 12 adults. Average user-independent sentence and word accuracies were 85.1% and 95.4%, respectively. To improve the accuracy when new users are introduced, we developed a progressive training model that can adapt to a new user's signing as they play the game. This approach produced a 5% absolute increase in sentence accuracy. To test for generality, a 13th user was recruited six months after the initial experiment and achieved similarly high accuracies. These promising results suggest that our recognizer will be sufficiently accurate for verifying children's signing while playing Adaptive CopyCat.