PostureChair: A Real-Time, As-Needed Feedback System for Improving the Sitting Posture of Office Workers
Bibliographic record
Abstract
Computer users spend the vast majority of their time sitting, and poor posture in the workplace is an endemic issue.This work presents PostureChair, a posture detection system that uses contextual digital feedback to persuade users to improve their sitting posture.Two types of digital feedback, with varying amounts of information, were compared through a repeated measures study to determine how much information is necessary to improve posture and to appeal to the user.The results of the study showed participants' sitting posture improved significantly with both feedback types when compared to their posture with feedback disabled.Participants overwhelmingly preferred the more detailed feedback even though it did not clearly improve users' sitting posture beyond the simpler feedback.The PostureChair system was well received and demonstrates that contextual posture improvement is an effective and much-needed addition to the workplace.I would like to thank my research supervisor Anthony Whitehead for providing me with this opportunity, and for keeping me on track and on schedule.To my comrades-in-arms Rufino Ansara, Shannon Fenton and Colin Killby, you kept me sane when things were tough, gave me a hand when I needed it,
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".