Evaluation of live human-computer music-making: Quantitative and qualitative approaches

NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, [VOL 67,ISS 11(2009)] DOI: 10.1016/j.ijhcs.2009.05.00

A Multidimensional Continuous Contextual Lighting Control System Using Google Glass

An increasing number of internet-connected LED lighting fixtures and bulbs have recently become available. This development, in combination with emerging hardware and software solutions for activity recognition, establish an infrastructure for context-aware lighting. Automated lighting control could potentially provide a better user experience, increased comfort, higher productivity, and energy savings compared to static uniform illumination. The first question that comes to mind when thinking about context-aware lighting is how to determine the relevant activities and contexts. Do we need different lighting for reading a magazine and reading a book, or maybe just different lighting for reading versus talking on the phone? How do we identify the relevant situations, and what are the preferred lighting settings? In this paper we present three steps we took to answer these questions and demonstrate them via an adaptive five-channel solid-state lighting system with continuous contextual control. We implemented a multidimensional user interface for manual control as well as an autonomous solution using wearable sensors. We enable a simple set of sensors to manipulate complicated lighting scenarios by indirectly simplifying and reducing the complexity of the sensor-lighting control space using human-derived criteria. In a preliminary user study, we estimated significant energy savings of up to 52% and showed multiple future research directions, including behavioral feedback

Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces