Preserving Safety, Dignity and Autonomy Through Multi-Modal Interactions: An Exploration into a Preferred Future of Design for Accessible Dressing Technology and E-Textiles
Bibliographic record
Abstract
Ready-to-wear clothing is often designed for individuals without disabilities, which can create dressing barriers and challenges for individuals with disabilities, especially those with motor coordination, cognitive challenges sensory and self-regulation issues.In two studies we explored the visions of occupational therapists for the future of dressing through smart clothing and dressing technology.Using co-design and design fiction online workshops, we were able to gather a rich data set that included interview data, virtual sticky notes, creative writing exercises and storyboards.We coded and used thematic and abductive analysis to explore the data and created design fictions to explore the future of dressing technology.Occupational therapists identified challenges that impact dressing tasks, and requirements for the design of technology situated in the near future that would support the autonomy of disabled people.They identified that instructional technology (such as clothing that provides sequencing cues and task instructions) could offer people with disabilities support for dressing challenges.According to the OTs, dressing technologies of the future should be multi-modal, preserve the dignity of the individual, be discreet, washable and durable, encourage autonomy, maintain safety and security, be customizable and be funded by governments or social groups.There are many people I wish to thank for their support and encouragement over the course of this thesis.Firstly, I wish to thank my thesis supervisors Dr. Audrey Girouard and Prof. Chantal Trudel, both of you have inspired and challenged me to grow and explore new avenues of knowledge in HCI and design.I am grateful for your neverending flexibility, wealth of knowledge and encouragement.You have both inspired me to expand my horizons and guided me through so much new learning in the midst of a global pandemic.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".