Effects of Pinch Technique, Torque Direction and Sensation on the Pinch Force in Loaded and Unloaded Conditions
Bibliographic record
Abstract
Many daily hand-related tasks require the ability to produce and control pinch forces to handle small objects. While there have been studies of various parameter effects on grip force, there appear to be no studies that investigate these effects on pinch force. This study aims to determine the effects of pinch technique, torque direction and sensation on pinch force with an emphasis on screw knobs under loaded and unloaded conditions. A total of 30 manual workers participated in the study. The workers were required to operate loaded and unloaded screw knobs in clockwise and counterclockwise directions to produce pinch forces using reduced and increased sensations and 3 types of common pinch techniques. The data was analysed using the analysis of variance via Minitab 16. Results for both conditions showed that pinch force is significantly affected by torque direction, sensation and pinch technique. However, the interaction effects of sensation and torque direction on pinch force appeared to be insignificant for both conditions. This is because the effects of increased sensation which reduce pinch force were neutralised by the effects of clockwise torque direction which increase force, while the effects of reduced sensation which increase pinch force were compensated by the effects of counterclockwise rotations which reduce pinch force. This study serves as a fundamental guideline for researchers and designers to improve hand tool designs operated with pinch grips so that they are safer and more ergonomic for manual tasks in the industry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".