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Record W7115244912

Wearable health technology design:A humanist accessory approach

2017· article· en· W7115244912 on OpenAlexfundno aff

Bibliographic record

VenueEdinburgh Research Explorer (University of Edinburgh) · 2017
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilTrent UniversityNottingham Trent University
KeywordsWearable computerHumanismWearable technologyNarrativeFunction (biology)Presentation (obstetrics)Participatory design
DOInot available

Abstract

fetched live from OpenAlex

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.”

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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0040.003
Scholarly communication0.0000.004
Open science0.0080.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.262
GPT teacher head0.392
Teacher spread0.130 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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