Publications
カンファレンス (国際) The Effectiveness of Path-segmentation for Modeling Lasso Times in Width-varying Paths
Shota Yamanaka, Hiroki Usuba, Wolfgang Stuerzlinger (Simon Fraser University), Homei Miyashita (Meiji University)
The 2022 ACM Interactive Surfaces and Spaces Conference (ISS 2022)
2022.11.20
Models of lassoing time to select multiple square icons exist, but realistic lasso tasks also typically involve encircling non-rectangular objects. Thus, it is unclear if we can apply existing models to such conditions where, e.g., the width of the path that users want to steer through changes dynamically or step-wise. In this work, we conducted two experiments where the objects were non-rectangular, with path widths that narrowed or widened, smoothly or step-wise. The results showed that the baseline models for pen-steering movements (the steering and crossing law models) fitted the timing data well, but also that segmenting width-changing areas led to significant improvements. Our work enables the modeling of novel UIs requiring continuous strokes, e.g., for grouping icons.
Paper :
The Effectiveness of Path-segmentation for Modeling Lasso Times in Width-varying Paths
（外部サイト）