Evaluating the Impact of Motion Sensing Errors on Ergonomic Analysis
Bibliographic record
Abstract
The use of motion sensing technologies for ergonomic analysis of worker motions has gained increasing attention in construction. Using motion capture data enables extracting ergonomic assessment inputs more accurately than through a human observer. Accordingly, methods of collecting and analyzing human motion data have been developed to automate the ergonomic evaluation process for effective identification of ergonomic risk factors associated with manual operations. However, despite advancements in motion capture technologies, there is still inaccuracy associated with the resulting motion capture data, which leads to impreciseness of the output of the ergonomic assessment. This study investigates the impact that the imprecision of the motion capture data has on the results of ergonomic analysis, to evaluate the technical feasibility of a motion sensing approach to ergonomic analysis and to discuss the potential solutions by incorporating sensing errors into the motion analysis. Specifically, different possible sensing errors pertaining to a body joint location have been considered and the sensitivity of the errors on the results of ergonomic evaluation has been quantified. The results can be used to obtain an accurate and realistic adjustment for the results of ergonomic assessment based on the amount of error associated with any motion capture technology.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".