The Roles of Whole Body Balance, Shoe-Floor Friction, and Joint Strength During Maximum Exertions: Searching for the “Weakest Link”
Bibliographic record
Abstract
Exerting manual forces is critical during occupational performance. Therefore, being able to estimate maximum force capacity is particularly useful for determining how these manual exertion demands relate to available capacity. To facilitate this type of prediction requires a complete understanding of how maximum force capacity is governed biomechanically. This research focused on identifying how factors including joint moment strength, balance and shoe-floor friction affected hand force capacity during pulling, pressing downward and pushing medially. To elucidate potential limiting factors, joint moments were calculated and contrasted with reported joint strength capacities, the balancing point within the shoe-floor interface was calculated and expressed relative to the area defined by the shoe-floor interface, and the net applied horizontal forces were compared with the available friction. Each of these variables were calculated as participants exerted forces in a series of conditions designed to systematically control or restrict certain factors from limiting hand force capacity. The results demonstrated that hand force capacity, in all tested directions, was affected by the experimental conditions (up to 300%). Concurrently, biomechanical measures reached or surpassed reported criterion thresholds inferring specific biomechanical limitations. Downward exertions were limited by elbow strength, whereas pulling exertions were often limited by balance along the anterior-posterior axis. No specific limitations were identified for medial exertions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".