Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Reimagining Machine Learning's Role in Assistive Technology by Co-Designing Exergames with Children Using a Participatory Machine Learning Design Probe
Northern Arizona University.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.ORCID iD: 0000-0002-1769-0138
Universidad Carlos III de Madrid.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media, Human-Computer Interaction.
Show others and affiliations
2023 (English)In: The 25th International ACM SIGACCESS Conference on Computers and Accessibility / [ed] Erin Brady; Maria Wolters, Association for Computing Machinery (ACM), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The paramount measure of success for a machine learning model has historically been predictive power and accuracy, but even a gold-standard accuracy benchmark fails when it inappropriately misrepresents a disabled or minority body. In this work, we reframe the role of machine learning as a provocation through a case study of participatory work co-creating exergames by employing machine learning and its training as a source of play and motivation rather than an accurate diagnostic tool for children with and without Sensory Based Motor Disorder. We created a design probe, Cirkus, that supports nearly any aminal locomotion exergame while collecting movement data for training a bespoke machine learning model. During 5 participatory workshops with a total of 30 children using Cirkus, we co-created a catalog of 17 exergames and a resulting machine-learning model. We discuss the potential implications of reframing machine learning’s role in Assistive Technology for values other than accuracy, share the challenges of using “messy” movement data from children with disabilities in an everchanging co-creation context for training machine learning, and present broader implications of using machine learning in therapy games.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023.
Keywords [en]
Sensory Based Motor Disorder, Participatory Machine Learning, Designing with Children, Physical Therapy, Play, Games
National Category
Human Aspects of ICT Interaction Technologies
Research subject
Human-Computer Interaction
Identifiers
URN: urn:nbn:se:uu:diva-511833DOI: 10.1145/3597638.3608421ISBN: 979-8-4007-0220-4 (print)OAI: oai:DiVA.org:uu-511833DiVA, id: diva2:1797757
Conference
The 25th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2023, 23-25 October, New York City, New York, USA
Available from: 2023-09-15 Created: 2023-09-15 Last updated: 2023-09-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textConference information

Authority records

Turmo Vidal, LaiaWaern, Annika

Search in DiVA

By author/editor
Turmo Vidal, LaiaWaern, Annika
By organisation
Human-Computer Interaction
Human Aspects of ICTInteraction Technologies

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 174 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf