Vision-Based Computing Pipeline for Recognizing Hand Grip-Types During Tool Handling
Bibliographic record
Abstract
OCCUPATIONAL APPLICATIONSHands are frequently exposed to the risks of musculoskeletal disorders (MSDs) due to their involvement in tool handling. We propose a computer vision-based pipeline that can accurately recognize the types of hand grips during tool handling using monocular red, green, and blue (RGB) images. While the predominant methods for hand ergonomics assessments require considerable training for practitioners, the proposed pipeline can facilitate assessments by providing crucial information, such as grip types, duration, and repetition, in a continuous and noninvasive manner. The proposed pipeline could support preventative measures, early diagnosis, and effective treatments for hand-related MSDs in workspaces. Additionally, the simple setup of the proposed pipeline can be integrated with other ergonomics assessment tools that are not limited to the hands, contributing to a more comprehensive analysis of MSD risks across body parts.
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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 | 0.001 | 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".