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UbiComp2021顶会

Teaching American Sign Language in Mixed Reality

Qijia Shao, Amy Sniffen, Julien Blanchet, Megan E. Hillis, Xinyu Shi, Themistoklis K. Haris, Jason Liu, Jason Lamberton, Melissa Malzkuhn, Lorna C. Quandt, James Mahoney, David J. M. Kraemer

2021年份
46被引次数
3顶会引用

摘要

This paper presents a holistic system to scale up the teaching and learning of vocabulary words of American Sign Language (ASL). The system leverages the most recent mixed-reality technology to allow the user to perceive her own hands in an immersive learning environment with first-and third-person views for motion demonstration and practice. Precise motion sensing is used to record and evaluate motion, providing real-time feedback tailored to the specific learner. As part of this evaluation, learner motions are matched to features derived from the Hamburg Notation System (HNS) developed by sign-language linguists. We develop a prototype to evaluate the efficacy of mixed-reality-based interactive motion teaching. Results with 60 participants show a statistically significant improvement in learning ASL signs when using our system, in comparison to traditional desktop-based, non-interactive learning. We expect this approach to ultimately allow teaching and guided practice of thousands of signs.

CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computer systems organization → Embedded systems.

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