Glove Attributes and Their Contribution to Force Decrement and Increased Effort in Power Grip
Bibliographic record
Abstract
OBJECTIVE: To determine the contribution of the loss of tactile sensitivity, glove flexibility, glove thickness, and changes in finger geometry to force decrement and increased effort during gloved power grip. BACKGROUND: Gloved work has been shown to increase the effort required to perform manual tasks. METHOD: A battery of maximal and submaximal gripping tasks was performed while grip force and surface electromyography of seven forearm muscles were recorded. Participants performed power grips while wearing three different thicknesses of rubber gloves (differing only in thickness; maximum 3.1 mm), wearing interdigital spacers between the fingers (matched to the glove thicknesses), and with a bare hand. RESULTS: Decreases in maximum grip force compared with the bare hand were observed for the thickest glove (-31.0 +/- 6.8%, p < .05) and for the thickest interdigital spacers (-9.7 +/- 5.9%, p < .05). Participants increased their grip force with increasing glove thickness for a submaximal object-lifting task (p < .01). To maintain an unloaded grip posture and to create a fixed submaximal force, participants increased muscle activation (p < .05) for all muscles with increasing glove thickness. CONCLUSION: Decreases in maximal grip force and increased effort in submaximal tasks could be attributed to a combination of reduced tactile sensitivity, the effort to bend the gloves, and interdigital separation. APPLICATION: Although the values obtained are specific to the rubber gloves tested, the results give insights into factors important in the design and selection of gloves.
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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 | 0.001 | 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".