Different Phases in Manual Materials Handling Have Different Performance Criteria: Evidence From Multi-Objective Optimization
Bibliographic record
Abstract
A manual material handling task involves the phases of reaching, lifting, unloading, and standing up (RLUS). Understanding the mechanisms of manual material handling is important for occupational health and the development of assist devices. Predictive models are becoming popular in exploring which performance criterion is appropriate in the lifting phase. However, limited attempts have been performed on the other phases. The associated performance criterion for predicting other phases is unknown. In this study, an optimization model for predicting RLUS has been developed with the multi-objective optimization method. Two performance criteria (minimum dynamic effort and maximum balance) were studied to explore their importance in each phase. The result shows that maximum balance leads to joint angle errors 27.6% and 40.9% smaller than minimum dynamic effort in reaching and unloading phases, but 40.4% and 65.9% larger in lifting and standing up phases. When the two performance criteria are combined, the maximum balance could help improve the predicting accuracy in the reaching, lifting, and unloading phases. These findings suggest that people prefer different performance criteria in different phases. This study helps understand the differences in motion strategies in manual materials handling (MMH), which would be used to develop a more accurate predictive model.
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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 | 0.000 | 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".