Keywords

Virtual reality, visualization, movement, accuracy

Abstract

Virtual reality (VR) has become a powerful tool for motor learning and skill acquisition, offering immersive environments for users to practice and refine movements. This thesis investigates how different visualization styles in VR affect movement imitation accuracy, specifically focusing on hand movements. While prior research has explored precise alignment and visualization individually, few studies have examined their combined impact. This study addresses that gap by evaluating the effectiveness of various visualization methods in relation to offset, animation, and manual type.

We developed an application to ensure all participants experienced each visualization factor as 12 combinations in varied sequences. The user study conducted with 30 participants combined performance data with responses from the qualification, between trial, and end of experiment questionnaires. Movement data assessed performance accuracy, and questionnaire data captured user perception.

The results indicate that manual type significantly affects user satisfaction and accuracy (p < 0.001), with the unimanual condition yielding the highest accuracy. Animation style also had a significant effect (p < 0.001), with discrete animations improving accuracy compared to continuous animations. Offset had no significant effect on accuracy, but users did prefer closer visualizations.

These findings provide valuable insights into VR-based motor learning applications. By using discrete animation and close-up visuals, developers can enhance the effectiveness of movement learning tools. This could have a direct impact on careers where muscle memory is a necessity. Future research could explore applications in rehabilitation, training, and remote teleoperation to optimize VR-guided motor tasks. Research could also evaluate the impact of visualization design in VR on real-world applications.

Completion Date

2025

Semester

Spring

Committee Chair

LaViola II, Joseph

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Department of Computer Science

Identifier

DP0029393

Document Type

Dissertation/Thesis

Campus Location

Orlando (Main) Campus

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