Metrics of Motor Learning for Analyzing Movement Mapping in Virtual Reality

要旨

Virtual reality (VR) techniques can modify how physical body movements are mapped to the virtual body. However, it is unclear how users learn such mappings and, therefore, how the learning process may impede interaction. To understand and quantify the learning of the techniques, we design new metrics explicitly for VR interactions based on the motor learning literature. We evaluate the metrics in three object selection and manipulation tasks, employing linear-translational and nonlinear-rotational gains and finger-to-arm mapping. The study shows that the metrics demonstrate known characteristics of motor learning similar to task completion time, typically with faster initial learning followed by more gradual improvements over time. More importantly, the metrics capture learning behaviors that task completion time does not. We discuss how the metrics can provide new insights into how users adapt to movement mappings and how they can help analyze and improve such techniques.

受賞
Honorable Mention
著者
Difeng Yu
University of Copenhagen, Copenhagen, Denmark
Mantas Cibulskis
University of Copenhagen, Copenhagen, Denmark
Erik Skjoldan. Mortensen
University of Copenhagen, Copenhagen, Denmark
Mark Schram Christensen
University of Copenhagen, Copenhagen, Denmark
Joanna Bergström
University of Copenhagen, Copenhagen, Denmark
論文URL

https://doi.org/10.1145/3613904.3642354

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Movement and Motor Learning B

311
4 件の発表
2024-05-16 20:00:00
2024-05-16 21:20:00