Designing and Building a 3D Human Motion Dataset for Vision-Based Ergonomics Risk Assessments
Bibliographic record
Abstract
Artificial Intelligence (AI) is increasingly used in ergonomics, particularly for assessing musculoskeletal disorder (MSD) risks. Recent advancements in vision-based AI have enabled the monitoring of MSD risks using ordinary cameras, providing more accessible and less intrusive alternatives to traditional observation-based methods. However, existing AI models, trained on generic computer vision-domain datasets, lack the keypoints necessary for calculating intricate angles in high-degree-of-freedom (DoF) joints. We present the design and building process of a large-scale 3D human motion dataset designed to train vision-based AI models for ergonomics risk assessments. The dataset captures 47-keypoint 3D human pose selected for high-DoF joint angle calculations and vision-based pose estimation, capturing 7 million frames of 10 subjects performing 9 categories of manual material handling tasks. A baseline MotionBert model trained on our dataset achieved a mean absolute angle error of 3.5° and demonstrated its generalization capability on real-world industry videos.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".