Theoretical action laws and quantitative models are foundational to any field. In human-computer interaction and ergonomics, Fitts' law is one of the theoretical foundations for interface and input device development and research. It has served as a theoretical framework for evaluating input devices, a tool for computational interface design, and a logical basis for modeling more complex HCI tasks. However, ample empirical evidence has shown that Fitts' law and other existing action laws encounter problems, or even fail, when modeling touch interaction, primarily because they do not account for the imprecision of finger touch in interaction. This research will establish robust new action laws for touch interaction, including both pointing and trajectory-based gesturing (steering), which will guide the design and evaluation of touch interfaces and serve as cornerstones for modeling complex tasks, computational interface design and optimization. Given the ubiquitous adoption of mobile devices where touch input is dominant, project outcomes are expected to have broad impact that will reach millions of users. To demonstrate the practical value and effectiveness of the new touch action laws, they will be used to quantify the touch capacity of older adults, thereby laying the foundation for designing touch interfaces well-suited for that user community.
The starting point for this research will be the principal investigator's Finger-Fitts (FFitts) law derived from the Dual Gaussian Distribution Model and Fitts' law. This preliminary work will be expanded to model 2D target selection and gesturing tasks. The research will be carried out following standard practices in Human Computer Interaction (HCI), first deriving candidate models from existing models, hypotheses, and rational assumptions, and then conducting rigorous user studies to evaluate the new models. The experimental results will in turn be used to refine the new models, which again will be evaluated via studies. Project outcomes will include the following theoretical and empirical intellectual contributions: action laws for touch pointing, including both the task form of the FFitts law which will predict touch pointing time with nominal task parameters (i.e., finger travel distance A and target width W), and the bivariate FFitts law which will model 2D pointing tasks on rectangular targets such as buttons, check boxes and hyperlinks; action laws for trajectory gestures (Finger-Steering laws), including a basic form for steering along straight paths and a generic form for other paths; and a touch action law based understanding of older adults' touch interaction capacity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
