Wearable health technology design:A humanist accessory approach
Bibliographic record
Abstract
This article presents the “accessory approach,” conceived as a holistic form of body adornment, not only associated with fashion, but also as a design approach which includes a wearer’s physical, psychological and social preferences. We propose this as a humanistic design philosophy which may inform the design of future wearable health technology, in contrast to increasing trends toward the medicalization and quantification of people’s whole lives. At the same time, there is a pragmatic case to be made for more human-centred approaches to the design of assistive technologies for the body, which are frequently rejected by end users due to poor cultural (as well as physical) fit.We examine the potential socio-phenomenological framework offered by the accessory as a relational category of both expressive and functional objects. Using Cunningham’s framework of narrative contemporary jewellery, we analyse three projects, and show how the accessory can function as a complex platform to support relations between maker, wearer and viewer. Finally, we relate this approach to the debate in interactive wearable design regarding the visibility of technology on the body, and propose a shift from designing wearable health technologies with minimal “social weight,” to providing a relational platform capable of supporting what we have termed “social agility.”
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".