Capturing Distalization

Rose Stamp, Lilyana Khatib, Hagit Hel-Or


Abstract
Coding and analyzing large amounts of video data is a challenge for sign language researchers, who traditionally code 2D video data manually. In recent years, the implementation of 3D motion capture technology as a means of automatically tracking movement in sign language data has been an important step forward. Several studies show that motion capture technologies can measure sign language movement parameters – such as volume, speed, variance – with high accuracy and objectivity. In this paper, using motion capture technology and machine learning, we attempt to automatically measure a more complex feature in sign language known as distalization. In general, distalized signs use the joints further from the torso (such as the wrist), however, the measure is relative and therefore distalization is not straightforward to measure. The development of a reliable and automatic measure of distalization using motion tracking technology is of special interest in many fields of sign language research.
Anthology ID:
2022.signlang-1.29
Volume:
Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
187–191
Language:
URL:
https://aclanthology.org/2022.signlang-1.29
DOI:
Bibkey:
Cite (ACL):
Rose Stamp, Lilyana Khatib, and Hagit Hel-Or. 2022. Capturing Distalization. In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, pages 187–191, Marseille, France. European Language Resources Association.
Cite (Informal):
Capturing Distalization (Stamp et al., SignLang 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.signlang-1.29.pdf