Development and Evaluation of a Low-Jitter Hand Tracking System for Improving Typing Efficiency in a Virtual Reality Workspace

Résumé fourni par la source

Virtual reality technology promises to transform immersive experiences across various applications, particularly within office environments. Despite its potential, the challenge of achieving efficient text entry in virtual reality persists. This study addresses this obstacle by introducing a novel machine learning-based solution, namely, the two-stream long short-term memory typing method, to enhance text entry performance in virtual reality. The two-stream long short-term memory method utilizes the back-of-the-hand image, employing a long short-term memory network and a Kalman filter to enhance hand position tracking accuracy and minimize jitter. Through statistical analysis of the data collected in the experiment and questionnaire results, we confirmed the effectiveness of the proposed method. In addition, we conducted an extra experiment to explore the differences in users’ typing behavior between regular typing and virtual reality-based typing. This additional experiment provides valuable insights into how users adapt their typing behavior in different environments. These findings represent a significant step in advancing text entry within virtual reality, setting the stage for immersive work experiences in office environments and beyond.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.