Development of a framework to implement time analysis in digital human modeling systems using predetermined motion time systems
Bibliographic record
Abstract
Abstract Digital human modeling (DHM) systems are increasingly utilized to design and optimize human work processes. A key challenge in employing DHM systems is precisely estimating task times for activities depicted through 3D human models. This study introduces an automated approach for time analysis derived from DHM data, focusing on integrating the Maynard Operation Sequence Technique (MOST) time system. Traditional time analysis using MOST often requires extensive manual effort from trained analysts to evaluate task execution, which can be both time-consuming and costly, particularly when applied to 3D designs. However, the automated methodology presented in this study significantly streamlines this process by minimizing manual intervention, thereby facilitating the creation of efficient and ergonomic human work processes during the design phase. The proposed method offers a systematic approach to integrating the data required for a Predetermined Motion Time System (PMTS) analysis, such as MOST, within a 3D environment. The study specifies the data that simulation tools can automatically generate for time analysis and highlights where manual input is needed during DHM simulation. By combining automated data with manual input, the method ensures a complete PMTS analysis. The method was validated through a field study, showing acceptable performance compared to both MOST estimates and actual observed task times. To illustrate its application, the method is implemented using Dassault Systèmes Delmia Ergonomic Workplace Design (EWD) software in a case example. EWD enables automatic time estimations for 3D-designed tasks while allowing for comprehensive ergonomic assessments. This multifaceted analysis equips design engineers with a powerful tool to evaluate design effectiveness, significantly saving time and resources before creating a physical prototype.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".