Dataset used in the CHI 2021 Student Research Competition Submission "CopyCat: Using Sign Language Recognition to Help Deaf Children Acquire Language Skills".
This dataset contains AlphaPose, Kinect, and Mediapipe features extracted from 3914 4K RGB+D videos of ASL phrases and is intended for use by the interactive game CopyCat. In total, 8 users (novice adult ASL signers) are included and can be identified as p1 ... p8. In addition, 58 phrases ranging from 3 to 5 words in length were recorded at least 6 times for each user.
All data is of the form MM-DD-YY_p(user id)_4KDepth/(phrase)/(instance of phrase)/MM-DD-YY_p(user_id)_4K.(phrase).(instance of phrase).(file extension)
For further details, please refer to the paper/poster, the ASL recognition toolkit system ASLRT (ASL Recognition Toolkit), or the ASL recognition data generation system.
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Dataset of adult novice signers created by CopyCat (Contextual Computing Group/Georgia Tech). Used in CHI 2021 Student Research Competition.
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