The analysis of button press dynamics (BPD) may reveal more detailed information about decisions in human–computer interaction. One huge advantage of a BPD-specific analysis lies in its tangible nature. More precisely, each button press (BP) constitutes a two-dimensional signal consisting of time and intensity coordinates, which can be easily illustrated. Moreover, one can also interpret BP signals by extracting several intuitive features, such as the duration and the maximum intensity. In this study, we analyse the characteristics of such intuitive BPD features. To this end, we conduct cluster analysis experiments with the following evaluation protocol. First, for each person-specific set of BPs, we will define ground truth (GT) clusterings by evaluating the BPs as time series and applying dynamic time warping (DTW) for the calculation of distances between two instances. Subsequently, we will extract a small set of intuitive features. Finally, we will compute the similarity between the DTW- and feature-based clusterings, based on two popular similarity measures. The outcomes of our experiments lead to the following observation. Extending binary BP information by one additional feature, i.e. the maximum press intensity, can already significantly improve the analysis of BP evaluations.
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