Accessible American Sign Language Learning in Virtual Reality via Inverse Kinematics

Along with the rapid advancement of Virtual Reality (VR) and the metaverse, interest in this technology has surged among game developers and in fields such as education and healthcare. VR has enabled the rise in immersive, gamified activities, whether for rehabilitation, therapy, or learning. Additionally, VR and Motion Capture (MoCap) have allowed developers to create further accessibility features for end-users with special needs. However, the excitement of using new technology often does not align with the end user’s use cases. The over-reliance on cutting-edge hardware can negatively impact most end users who lack access to such expensive tools. To this end, we conducted an inclusivity-focused study that enables learners to practice ASL in an immersive and engaging way using only head- and controller-based tracking. Our approach replaces full-body MoCap with Inverse Kinematics (IK) and simple controller mappings for upper-body pose and hand-gesture recognition, providing a low-cost, reproducible alternative to costly setups.

Authors

Institutions

Publication Details

Journal
Virtual Worlds
Published
2025-12-04
DOI
https://doi.org/10.3390/virtualworlds4040057
Citations
3
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
5.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accessible American Sign Language Learning in Virtual Reality via Inverse Kinematics

Jonnathan Berrezueta-Guzmán, Joshua Immanuel
3 citations
Virtual Worlds
Hand Gesture Recognition Systems
5.81
article

Accessible American Sign Language Learning in Virtual Reality via Inverse Kinematics

Jonnathan Berrezueta-Guzmán, Joshua Immanuel
article en
3 citations

Abstract

Along with the rapid advancement of Virtual Reality (VR) and the metaverse, interest in this technology has surged among game developers and in fields such as education and healthcare. VR has enabled the rise in immersive, gamified activities, whether for rehabilitation, therapy, or learning. Additionally, VR and Motion Capture (MoCap) have allowed developers to create further accessibility features for end-users with special needs. However, the excitement of using new technology often does not align with the end user’s use cases. The over-reliance on cutting-edge hardware can negatively impact most end users who lack access to such expensive tools. To this end, we conducted an inclusivity-focused study that enables learners to practice ASL in an immersive and engaging way using only head- and controller-based tracking. Our approach replaces full-body MoCap with Inverse Kinematics (IK) and simple controller mappings for upper-body pose and hand-gesture recognition, providing a low-cost, reproducible alternative to costly setups.

Virtual WorldsVol. 4(4)
Heilbronn University (DE)
Openalex Percentile: Top 4%
Hand Gesture Recognition Systems
5.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.