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Record W2765318865 · doi:10.1109/mprv.2017.3971123

Typhlex: Exploring Deformable Input for Blind Users Controlling a Mobile Screen Reader

2017· article· en· W2765318865 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 · 2017
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsGestureComputer scienceUsabilityHuman–computer interactionProcess (computing)Screen readerMobile deviceInput deviceMultimediaVisually impairedArtificial intelligenceWorld Wide WebComputer hardware

Abstract

fetched live from OpenAlex

Current smartphone technology presents many challenges for blind users. This article introduces the use of deformable inputs for blind users, which offers such users the ability to physically manipulate a device for system interaction. The authors describe the iterative design process of a deformable device prototype, Typhlex, with strategically placed grooves to elicit bend gestures. To understand its utility as a screen reader control, they conducted two exploratory studies with sighted participants (with the prototype hidden from view) and blind participants, focusing on comparing the usability of bend gestures to touch as primary forms of input. Their findings suggest that while easily learnable and enjoyed by both groups, the prototype had yet to improve blind users' performances when compared to the commonly used touch input paradigm. They present lessons learned from their design process and studies, and they discuss the promise of deformable input devices in the area of accessibility for blind users. This article is part of a special issue on physical computing and shape-changing interfaces.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.206
GPT teacher head0.355
Teacher spread0.149 · 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