Exploring user capability data with topological data analysis

U. Persad*, J. Goodman-Deane, P. M. Langdon, P. J. Clarkson

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

1 Citation (Scopus)

Abstract

This paper presents an analysis of user capability data using Topological Data Analysis (TDA) (unsupervised machine learning) to extract insight. The aim was to explore the global shape and sub-groupings (clusters of profiles) of people using data collected from the Cambridge Better Design Pilot Study of 362 people from across England and Wales. The resulting topological network demonstrated the global shape of the sample and distribution of sensory, cognitive and motor capability across the sample. The TDA network was automatically grouped into 14 distinct clusters, and distinguishing features of each cluster was extracted. The results demonstrate the value of applying TDA to analyse and visualise user capability data, and it is proposed that the cluster descriptions could be used for developing empirically based design tools such as personas for Inclusive Design.

Original languageEnglish
Title of host publicationBreaking Down Barriers
Subtitle of host publicationUsability, Accessibility and Inclusive Design
PublisherSpringer
Pages41-50
Number of pages10
ISBN (Electronic)9783319750286
ISBN (Print)9783319750279
DOIs
Publication statusPublished - 19 Feb 2018
Externally publishedYes

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