Augmented Reality-Based Gesture Classification: A Data-Driven Analysis Using Meta Quest 3 Hand Tracking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F25%3A43932345" target="_blank" >RIV/60461373:22340/25:43932345 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11166585" target="_blank" >https://ieeexplore.ieee.org/document/11166585</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACDSA65407.2025.11166585" target="_blank" >10.1109/ACDSA65407.2025.11166585</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Augmented Reality-Based Gesture Classification: A Data-Driven Analysis Using Meta Quest 3 Hand Tracking

Popis výsledku v původním jazyce

This feasibility study investigates the use of standalone augmented reality (AR) headsets for gesture classification as a foundational component of sign language translation systems. A dataset of five distinct hand gestures was collected using the Meta Quest 3 headset, capturing 3D point rotations via the WebXR API in both single-user and multi-user settings. In the single-user setting, 100 recordings were captured for each of the five gestures. In the multi-user setting, gesture data was collected from 20 individuals, each performing five gestures with five repetitions. A complete processing pipeline was implemented for data preprocessing, labeling, and analysis. Feature selection combined Principal Component Analysis (PCA) with importance scores from a Random Forest (RF) classifier. Gesture classification was performed using standard machine learning models, namely RF and Support Vector Classification (SVC). In the single-user setting, RF achieved 99.26% accuracy and SVC reached 98.51%. In the multi-user setting, RF achieved 95.90% and SVC 95.55%. These results demonstrate that AR-based hand tracking offers strong potential for robust, contactless gesture recognition. Nevertheless, further research is required to evaluate its suitability for real-world assistive communication technologies.

Název v anglickém jazyce

Augmented Reality-Based Gesture Classification: A Data-Driven Analysis Using Meta Quest 3 Hand Tracking

Popis výsledku anglicky

This feasibility study investigates the use of standalone augmented reality (AR) headsets for gesture classification as a foundational component of sign language translation systems. A dataset of five distinct hand gestures was collected using the Meta Quest 3 headset, capturing 3D point rotations via the WebXR API in both single-user and multi-user settings. In the single-user setting, 100 recordings were captured for each of the five gestures. In the multi-user setting, gesture data was collected from 20 individuals, each performing five gestures with five repetitions. A complete processing pipeline was implemented for data preprocessing, labeling, and analysis. Feature selection combined Principal Component Analysis (PCA) with importance scores from a Random Forest (RF) classifier. Gesture classification was performed using standard machine learning models, namely RF and Support Vector Classification (SVC). In the single-user setting, RF achieved 99.26% accuracy and SVC reached 98.51%. In the multi-user setting, RF achieved 95.90% and SVC 95.55%. These results demonstrate that AR-based hand tracking offers strong potential for robust, contactless gesture recognition. Nevertheless, further research is required to evaluate its suitability for real-world assistive communication technologies.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

ISBN

979-8-3315-3563-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Antalya

Datum konání akce

7. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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