Figure 1. Four users showing different typing behaviours involving different numbers of fingers and movement strategies. This paper reports typing rates, gaze and movement strategies for everyday typists, including both professionally trained and self-taught typists. We explain how untrained typists are able to type at very high rates, which were previously attributed only to the touch typing system that enforces the use of all 10 fingers.

We present a flexible Machine Learning approach for learning user-specific touch input models to increase touch accuracy on mobile devices. The model is based on flexible, non-parametric Gaussian Process regression and is learned using recorded touch inputs. We demonstrate that significant touch accuracy improvements can be obtained when either raw sensor data is used as an input or when the device's reported touch location is used as an input, with the latter marginally outperforming the former. We show that learned offset functions are highly nonlinear and user-specific and that user-specific models outperform models trained on data pooled from several users. Crucially, significant performance improvements can be obtained with a small (≈ 200) number of training examples, easily obtained for a particular user through a calibration game or from keyboard entry data.

Users often struggle to enter text accurately on touchscreen keyboards. To address this, we present a flexible decoder for touchscreen text entry that combines probabilistic touch models with a language model. We investigate two different touch models. The first touch model is based on a Gaussian Process regression approach and implicitly models the inherent uncertainty of the touching process. The second touch model allows users to explicitly control the uncertainty via touch pressure. Using the first model we show that the character error rate can be reduced by up to 7% over a baseline method, and by up to 1.3% over a leading commercial keyboard. Using the second model we demonstrate that providing users with control over input certainty reduces the amount of text users have to correct manually and increases the text entry rate.

Real-Time Optimisation Sketchploration Environment Figure 1: Sketchplorer is an interactive layout sketching tool supported by real-time model-based optimisation. The tool is designed to facilitate the creative and problem-solving aspects of sketching without requiring extensive input. While a designer is sketching, a design task is automatically inferred. The optimiser uses predictive models to make suggestions for local and global changes that improve usability and aesthetics. Suggestions appear on the side, and never override the designer's work.

We present the participatory design process of a robotic tutor of assistive sign language for children with autism spectrum disorder (ASD). Robots have been used in autism therapy, and to teach sign language to neurotypical children. The application of teaching assistive sign language-the most common form of assistive and augmentative communication used by people with ASD-is novel. The robot's function is to prompt children to imitate the assistive signs that it performs. The robot was therefore co-designed to appeal to children with ASD, taking into account the characteristics of ASD during the design process: impaired language and communication, impaired social behavior, and narrow flexibility in daily activities. To accommodate these characteristics, a multidisciplinary team defined design guidelines specific to robots for children with ASD, which were followed in the participatory design process. With a pilot study where the robot prompted children to imitate nine assistive signs, we found support for the effectiveness of the design. The children successfully imitated the robot and kept their focus on it, as measured by their eye gaze. Children and their companions reported positive experiences with the robot, and companions evaluated it as potentially useful, suggesting that robotic devices could be used to teach assistive sign language to children with ASD.