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Record W2756171776 · doi:10.1145/3131892

Smartwatch Wearing Behavior Analysis

2017· article· en· W2756171776 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

VenueProceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2017
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsSmartwatchWearable computerHuman–computer interactionWearable technologyComputer scienceMultimediaEmbedded system

Abstract

fetched live from OpenAlex

Smartwaches are the representative wearable or body-worn devices that provide convenient and easy information access. There is a growing body of research work on enabling novel interaction techniques and understanding user experiences of smartwatches. However, there is still lack of user experience research on wearing behaviors of smartwatches, which is critical for wearable device and service design. In this work, we investigate how college students wear smartwatches and what factors affect wearing behaviors by analyzing a longitudinal activity dataset collected from 50 smartwatch users for 203 days. Our results show that there are several temporal usage patterns and distinct groups of usage patterns. The factors affecting wearing behaviors are contextual, nuanced, and multifaceted. Our findings provide diverse design implications for improving wearability of smartwatches and leveraging smartwatches for behavioral changes.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
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 score0.485
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0060.006
Research integrity0.0000.001
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 head0.019
GPT teacher head0.291
Teacher spread0.273 · 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