Perception and biomechanics data in a manual handling task: a comparative study
Bibliographic record
Abstract
This paper explores the use of subjective perception tasks and its correlations with biomechanical data in the evaluation of manual material handling. Three main dimensions were considered for perception: physical regroups sensations issued from a specific body area; operative regroups feelings related to the execution of the task; and performance regroups feelings that involve a judgement on the execution or reflect overall sensations. The following questions were then explored. To what extent are perception data related to biomechanics data? Do both approaches lead to similar conclusions or interpretations when effect of practice, format and off-centre were tested? How can they complement one another? The task consisted of transferring 50 series of three 15 kg loads in order to verify the impact of free practice, format (box/cylinder) and load centre of gravity position. Eleven subjects rated perception on a CR-10 scale (Borg 1982) after each series. The session was completed with an interview on perception. The net resulting moment was systematically found to be the best correlated with data perception. While all physical and performance items corresponded in various ways to biomechanics data, perceptions associated with operative dimension appeared to be less related with biomechanical data. As regards the impact of practice, format and off-centre, both approaches would lead to the same conclusions, except for the effect of the off-centre. Verbal data add rational information about how or why perception can or cannot be reflected in biomechanics data. How both approaches can be matched more closely in manual handling is discussed.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".