TY - GEN

T1 - Metrics of Motor Learning for Analyzing Movement Mapping in Virtual Reality

AU - Yu, Difeng

AU - Cibulskis, Mantas

AU - Mortensen, Erik Skjoldan

AU - Christensen, Mark Schram

AU - Bergström, Joanna

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

U2 - 10.1145/3613904.3642354

DO - 10.1145/3613904.3642354

M3 - Article in proceedings

BT - CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

T2 - CHI '24: CHI Conference on Human Factors in Computing Systems

Y2 - 11 May 2024 through 16 May 2024

ER -