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Daniel Buschek, Florian Alt
TouchML: A Machine Learning Toolkit for Modelling Spatial Touch Targeting Behaviour
In IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces. Atlanta, GA, USA, March 29 - April 1, 2015. ACM, New York, NY, USA.
doi: 10.1145/2678025.2701381 (bib)

Pointing tasks are commonly studied in HCI research, for example to evaluate and compare different interaction techniques or devices. A recent line of work has modelled user-specific touch behaviour with machine learning methods to reveal spatial targeting error patterns across the screen. These models can also be applied to improve accuracy of touchscreens and keyboards, and to recognise users and hand postures. However, no implementation of these techniques has been made publicly available yet, hindering broader use in research and practical deployments. Therefore, this paper presents a toolkit which implements such touch models for data analysis (Python), mobile applications (Java/Android), and the web (JavaScript). We demonstrate several applications, including hand posture recognition, on touch targeting data collected in a study with 24 participants. We consider different target types and hand postures, changing behaviour over time, and the influence of hand sizes.