The keystroke-level model (KLM) is commonly used to predict the time it will take an expert user to accomplish a task without errors when using an interactive system. The KLM was initially intended to predict interactions in conventional set-ups, i.e., mouse and keyboard interactions. However, it has since been adapted to predict interactions with smartphones, in-vehicle information systems, and natural user interfaces. The simplicity of the KLM and its extensions, along with their resource- and time-saving capabilities, has driven their adoption. In recent years, the popularity of smartwatches has grown, introducing new design challenges due to the small touch screens and bimanual interactions involved, which make current extensions to the KLM unsuitable for modelling smartwatches. Therefore, it is necessary to study these interfaces and interactions. This paper reports on three studies performed to modify the original KLM and its extensions for smartwatch interaction. First, an observational study was conducted to characterise smartwatch interactions. Second, the unit times for the observed interactions were derived through another study, in which the times required to perform the relevant physical actions were measured. Finally, a third study was carried out to validate the model for interactions with the Apple Watch and Samsung Gear S3. The results show that the new model can accurately predict the performance of smartwatch users with a percentage error of 12.07%; a value that falls below the acceptable percentage dictated by the original KLM ~21%.

Interactive visualization plays a key role in the analysis of large datasets. It can help users to explore data, investigate hypotheses and find patterns. The easier and more tangible the interaction, the more likely it is to enhance understanding. This paper presents a tabletop Tangible User Interface (TUI) for interactive data visualization and offers two main contributions. First, we highlight the functional requirements for a data visualization interface and present a tabletop TUI that combines tangible objects with multi-touch interaction. Second, we compare the performance of the tabletop TUI and a multi-touch interface. The results show that participants found patterns faster with the TUI. This was due to the fact that they adopted a more effective strategy using the tabletop TUI than the multi-touch interface.