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Record W4400975355 · doi:10.1109/mprv.2024.3418899

Co-Designing Accessible Computer and Smartphone Input Using Physical Computing

2024· article· en· W4400975355 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Pervasive Computing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsCarleton University
FundersEngineering and Physical Sciences Research CouncilMicrosoft
KeywordsComputer scienceUbiquitous computingHuman–computer interactionContext-aware pervasive systemsMobile computingMultimediaComputer network

Abstract

fetched live from OpenAlex

Significant obstacles persist in meeting the accessibility needs of computer and smartphone users with mild-to-moderate upper limb motor impairments as they use their devices at work and home. Multimodal input can help, but has not been widely adopted. We build on existing literature with a discovery survey and semistructured follow-up interviews in which we identify common themes related to the limitations of today’s solutions and the ad hoc workarounds which are adopted. We ran a series of co-design workshop sessions to understand the potential of modern “physical computing” electronic device prototyping technologies to provide new and effective input options for our target user base. We present the resulting prototype solutions and describe the technology choices made. Finally, we discuss how the co-design process, in conjunction with access to suitable physical prototyping technologies, can be a powerful approach for designing accessibility-focused input systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.398
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it