A Human Body Posture Sensor for Monitoring and Diagnosing MSD Risk Factors
Bibliographic record
Abstract
A Human Body Posture Sensor for Monitoring and Diagnosing MSD Risk Factors A. Alwasel, K. Elrayes, E. Abdel-Rahman, C. Haas Pages 531-539 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Musculoskeletal disorders (MSDs) threaten the wellbeing and livelihood of a large number of construction workers incurring a significant cost to society. We present a new method to monitor and diagnose MSD risks in the workplace. The sensing unit of the system is an optical encoder encompassed within a non-intrusive exoskeleton to measure the joint angle of interest. This sensor can be applied to ball-and-socket and hinge-type joints of the human body, such as the shoulder, elbow, and knee joints. The system is contactless and does not require markers or cameras. Angle measurements are acquired directly without mathematical post-processing, thereby avoiding numerical noise and drift challenges. The system is a simple, robust, and deployable, but it currently lacks resolution of parallel degrees of freedom. Keywords: Motion tracking, Human joints, Angle measurement, MSD, Robotics, Gerontology DOI: https://doi.org/10.22260/ISARC2013/0057 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".