Developing an interactive overview for non-visual exploration of tabular numerical information

Abstract

This thesis investigates the problem of obtaining overview information from complex tabular numerical data sets non-visually. Blind and visually impaired people need to access and analyse numerical data, both in education and in professional occupations. Obtaining an overview is a necessary first step in data analysis, for which current non-visual data accessibility methods offer little support. This thesis describes a new interactive parametric sonification technique called High-Density Sonification (HDS), which facilitates the process of extracting overview information from the data easily and efficiently by rendering multiple data points as single auditory events. Beyond obtaining an overview of the data, experimental studies showed that the capabilities of human auditory perception and cognition to extract meaning from HDS representations could be used to reliably estimate relative arithmetic mean values within large tabular data sets. Following a user-centred design methodology, HDS was implemented as the primary form of overview information display in a multimodal interface called TableVis. This interface supports the active process of interactive data exploration non-visually, making use of proprioception to maintain contextual information during exploration (non-visual focus+context), vibrotactile data annotations (EMA-Tactons) that can be used as external memory aids to prevent high mental workload levels, and speech synthesis to access detailed information on demand. A series of empirical studies was conducted to quantify the performance attained in the exploration of tabular data sets for overview information using TableVis. This was done by comparing HDS with the main current non-visual accessibility technique (speech synthesis), and by quantifying the effect of different sizes of data sets on user performance, which showed that HDS resulted in better performance than speech, and that this performance was not heavily dependent on the size of the data set. In addition, levels of subjective workload during exploration tasks using TableVis were investigated, resulting in the proposal of EMA-Tactons, vibrotactile annotations that the user can add to the data in order to prevent working memory saturation in the most demanding data exploration scenarios. An experimental evaluation found that EMA-Tactons significantly reduced mental workload in data exploration tasks. Thus, the work described in this thesis provides a basis for the interactive non-visual exploration of a broad range of sizes of numerical data tables by offering techniques to extract overview information quickly, performing perceptual estimations of data descriptors (relative arithmetic mean) and managing demands on mental workload through vibrotactile data annotations, while seamlessly linking with explorations at different levels of detail and preserving spatial data representation metaphors to support collaboration with sighted users