Stephen Brewster

Ammar Al-Taie, Thomas Goodge, Shaun Macdonald et al.

Automated vehicles (AVs) must communicate their yielding intentions to pedestrians at crossings. External Human-Machine Interfaces (eHMIs, on-vehicle displays) are promising solutions, but were primarily tested with walking pedestrians. Runners are a significant pedestrian group who move faster and face distinct bodily and perceptual demands, raising questions about how pedestrian activity influences eHMI use. We conducted an outdoor study using an augmented reality simulator. Participants navigated a virtual crossing while walking and running; an approaching AV displayed one of three eHMIs: red/green colour-changing lights, animated cyan lights, or no-eHMI. No-eHMI consistently underperformed. Walkers mostly stopped and validated eHMI signals with vehicle behaviour; they processed both eHMI animations and colour changes effectively. Runners experienced greater time pressure to cross, increasing reliance on the eHMI over vehicle behaviour. They preferred colour changes over animation for rapid decisions. These findings are crucial for promoting eHMI inclusivity and physical wellbeing as AVs join our roads.

Satvik Venkatesh, David Moffat, Alexis Kirke et al.

Segmenting audio into homogeneous sections such as music and speech helps us understand the content of audio. It is useful as a pre-processing step to index, store, and modify audio recordings, radio broadcasts and TV programmes. Deep learning models for segmentation are generally trained on copyrighted material, which cannot be shared. Annotating these datasets is time-consuming and expensive and therefore, it significantly slows down research progress. In this study, we present a novel procedure that artificially synthesises data that resembles radio signals. We replicate the workflow of a radio DJ in mixing audio and investigate parameters like fade curves and audio ducking. We trained a Convolutional Recurrent Neural Network (CRNN) on this synthesised data and outperformed state-of-the-art algorithms for music-speech detection. This paper demonstrates the data synthesis procedure as a highly effective technique to generate large datasets to train deep neural networks for audio segmentation.

Thomas Pietrzak, Andrew Crossan, Stephen A. Brewster et al.

We propose a new technique to help users to explore geometric shapes without vision. This technique is based on a guidance using directional cues with a pin array. This is an alternative to the usual technique that consists of raising the pins corresponding to dark pixels around the cursor. In this paper we compare the exploration of geometric shapes with our new technique in unimanual and bimanual conditions. The users made fewer errors in unimanual condition than in bimanual condition. However they did not explore the shapes more quickly and there was no difference in confidence in their answer.

Thomas Pietrzak, Andrew Crossan, Brewster A. Stephen et al.

Spatial information can be difficult to present to a visually impaired computer user. In this paper we examine a new kind of tactile cueing for non-visual interaction as a potential solution, building on earlier work on vibrotactile Tactons. However, unlike vibrotactile Tactons, we use a pin array to stimulate the finger tip. Here, we describe how to design static and dynamic Tactons by defining their basic components. We then present user tests examining how easy it is to distinguish between different forms of pin array Tactons demonstrating accurate Tacton sets to represent directions. These experiments demonstrate usable patterns for static, wave and blinking pin array Tacton sets for guiding a user in one of eight directions. A study is then described that shows the benefits of structuring Tactons to convey information through multiple parameters of the signal. By using multiple independent parameters for a Tacton, this study demonstrates participants perceive more information through a single Tacton. Two applications using these Tactons are then presented: a maze exploration application and an electric circuit exploration application designed for use by and tested with visually impaired users.