TouchML: A Machine Learning Toolkit for Modelling Spatial Touch Targeting Behaviour-猫眼课题宝

TouchML：用于建模空间触摸目标行为的机器学习工具包

DOI：

10.1145/2678025.2701381

发表时间：

2015

期刊：

Proceedings of the 20th International Conference on Intelligent User Interfaces

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通讯作者：

中科院分区：

文献类型：

作者：

Daniel Buschek;Florian Alt

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.