Real-time visual feedback can cue changes in grip force during electric hand tool operation
Bibliographic record
Abstract
Objectives. Grip force applied during repetitive hand tool use varies amongst individuals and some apply more force than necessary. Augmented feedback may help modify grip force to reduce the risk of developing cumulative trauma disorders but has been scarcely investigated during electric hand tool operation. This study evaluated the feasibility of using real-time visual feedback to modify grip force and forearm electromyography (EMG) during electric hand tool operation. A secondary objective was to evaluate the effect of hand and tool orientation on any effects of visual feedback. Methods. Grip force and forearm muscle EMG were recorded as participants fastened bolts at three locations (low, high and overhead) using an electric pistol-grip nut-runner, without and with visual feedback. Results. Feedback decreased grip force (36.1% decrease; p < 0.001) and EMG of three wrist flexor muscles (22.8–33.0%; p < 0.008). Grip force and EMG also differed between fastening locations, but there were no interactions with condition (baseline and feedback; p > 0.266), suggesting that visual feedback can modify grip force across varying hand and tool orientations. Conclusion. Visual feedback can successfully modify grip force during hand tool operation. However, further investigation is needed to understand how to appropriately implement visual feedback during hand tool operation.
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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 | 0.000 | 0.004 |
| 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) | 0.003 | 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".