Extending Fitts’ Law: a model of stroke operations in commercial applications

Abstract

In the rapidly evolving landscape of smartphone technology and micro-interaction design, our research aims to extend the applicability of Fitts' Law to better understand and model the dynamics of finger stroke gestures on smartphone commercial applications. Traditional interpretations of Fitts' Law, which primarily rely on distance (D) and target width (W) to quantify task difficulty (ID) in gesture input, may not be sufficient in capturing the complexities inherent in touchscreen interactions with commercial applications. To address this research gap, we propose an adaptation of Fitts' Law that incorporates dynamic physical parameters to better align with the execution of single-finger stroke gestures. Our classification of stroke gestures into two distinct types and the introduction of three new parameters – initial swiping velocity (V_start), final swiping velocity (V_end), and maximum swiping acceleration (A_max) – form the core of our modified model. Through a series of controlled experiments, we validate our models, demonstrating clear distinctions between Type I and II stroke gestures and achieving a high level of predictive accuracy (R² > 0.9).Our findings highlight the significant influence of individual biomechanical differences on movement time (MT) within a single stroke gesture, underscoring that the performance of gestures is not solely determined by target dimensions (D and W) but also by individual factors. Despite the challenge in precisely calculating D and W for complex gesture designs, our study emphasizes that incorporating dynamic parameters effectively explains the observed gesture parameter randomness in commercial applications, thereby enhancing the ecological validity of Fitts' Law.However, acknowledging limitations, we recognize the need for further investigation into a broader range of multi-touch gestures, such as pinch-to-zoom and rotation. Additionally, it awaits a deeper exploration of how individual biomechanics, cognitive states, and task complexities interplay to influence gesture execution time. Future research should strive to fill these knowledge gaps, ultimately leading to a more holistic understanding of multi-touch gesture behavior and improved design guidelines for gestural interfaces.In conclusion, this research contributes a refined Fitts' Law model, which accurately represents two types of stroke gestures in commercial smartphone applications. We also explore individual differences, informing design principles for future gesture interactions.

Keywords: Fitts' law, stroke gesture, Physical Modeling, Finger Input

DOI: 10.54941/ahfe1005737