Touch interaction is an increasingly ubiquitous input modality on modern devices. It appears on devices including phones, tablets, smartwatches and even some recent laptops. Despite its popularity, touch as an input technology suffers from a high level of measurement uncertainty. This stems from issues such as the ‘fat finger problem’, where the soft pad of the finger creates an ambiguous contact region with the screen that must be approximated by a single touch point. In addition to these physical uncertainties, there are issues of uncertainty of intent when the user is unsure of the goal of a touch. Perhaps the most common example is when typing a word, the user may be unsure of the spelling leading to touches on the wrong keys. The uncertainty of touch leads to an offset between the user’s intended target and the touch position recorded by the device. While numerous models have been proposed to model and correct for these offsets, existing techniques in general have assumed that the offset is a deterministic function of the input. We observe that this is not the case — touch also exhibits a random component. We propose in this dissertation that this property makes touch an excellent target for analysis using probabilistic techniques from machine learning. These techniques allow us to quantify the uncertainty expressed by a given touch, and the core assertion of our work is that this allows useful improvements to touch interaction to be obtained. We show this through a number of studies. In Chapter 4, we apply Gaussian Process regression to the touch offset problem, producing models which allow very accurate selection of small targets. In the process, we observe that offsets are both highly non-linear and highly user-specific. In Chapter 5, we make use of the predictive uncertainty of the GP model when applied to a soft keyboard — this allows us to obtain key press probabilities which we combine with a language model to perform autocorrection. In Chapter 6, we introduce an extension to this framework in which users are given direct control over the level of uncertainty they express. We show that not only can users control such a system succesfully, they can use it to improve their performance when typing words not known to the language model. Finally, in Chapter 7 we show that users’ touch behaviour is significantly different across different tasks, particularly for typing compared to pointing tasks. We use this to motivate an investigation of the use of a sparse regression algorithm, the Relevance Vector Machine, to train offset models using small amounts of data.
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