Curves Ahead: Enhancing the Steering Law for Complex Curved Trajectories

要旨

The Steering Law has long been a fundamental model in predicting movement time for tasks involving navigating through constrained paths, such as in selecting sub-menu options, particularly for straight and circular arc trajectories. However, this does not reflect the complexities of real-world tasks where curvatures can vary arbitrarily, limiting its applications. This study aims to address this gap by introducing the total curvature parameter K into the equation to account for the overall curviness characteristic of a path. To validate this extension, we conducted a mouse-steering experiment on fixed-width paths with varying lengths and curviness levels. Our results demonstrate that the introduction of K significantly improves model fitness for movement time prediction over traditional models. These findings advance our understanding of movement in complex environments and support potential applications in fields like speech motor control and virtual navigation.

受賞
Best Paper
著者
Jennie J.Y.. Chen
University of British Columbia, Vancouver, British Columbia, Canada
Sidney S. Fels
University of British Columbia, Vancouver, British Columbia, Canada
DOI

10.1145/3706598.3713102

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713102

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Spatial Interactions

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7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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