A Human Body Posture Sensor for Monitoring and Diagnosing MSD Risk Factors
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.
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
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it