Improving SensAct’s Usability and Potential to Support Augmentative and Alternative Communication (AAC) Using Human-Centred Design Methods
Bibliographic record
Abstract
Individuals living with speech impairments may require an augmentative and alternative communication (AAC) device to be able to speak or interact with other people.The implementation of an AAC device is fundamental to facilitate effective communication, expand social interaction, and to be part of the community.A team of developers at Bruyère -Saint-Vincent Hospital has been developing an AAC device called SensAct to fulfill those needs.Despite extensive advancements, stakeholders at the hospital feel the technology has usability issues preventing it from being implemented and used by a wider target audience.This study aims to investigate factors influencing SensAct's usability, specifically, focusing on SensAct's user interface (UI) and usability issues that arise when configuring the system for healthcare clients.Using qualitative methods from human-centred design, this study identified systemic factors that influence AAC/SensAct implementation, and three key usability issues that may undermine SensAct's ability to reach a broader audience: time constraints, the use of complex technical terms, and complex UI with minimal support.These findings informed the development of design recommendations to develop SensAct's UI.At a broader level, this study provides a basic framework to support further studies on SensAct to accomplish the goals of healthcare workers to better meet the needs of their clients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".