Evaluating the Ergonomic Benefit of a Wrist Brace on Wrist Posture, Muscle Activity, Rotational Stiffness, and Peak Shovel-Ground Impact Force During a Simulated Tree-Planting Task
Bibliographic record
Abstract
Background Tree planters are at a high risk for wrist injury due to awkward postures and high wrist loads experienced during each planting cycle, specifically at shovel-ground impact. Wrist joint stiffness provides a measure that integrates postural and loading information. Objective The purpose of this study was to evaluate wrist joint stiffness requirements at the instant of shovel-ground impact during tree planting and determine if a wrist brace could alter muscular contributions to wrist joint stiffness. Method Planters simulated tree planting with and without wearing a brace on their planting arm. Surface electromyography (sEMG) from six forearm muscles and wrist kinematics were collected and used to calculate muscular contributions to joint rotational stiffness about the wrist. Results Wrist joint stiffness increased with brace use, an unanticipated and negative consequence of wearing a brace. As a potential benefit, planters achieved a more neutrally oriented wrist angle about the flexion/extension axis, although a less neutral wrist angle about the ulnar/radial axis was observed. Muscle activity did not change between conditions. Conclusion The joint stiffness analysis, combining kinematic and sEMG information in a biologically relevant manner, revealed clear limitations with the interface between the brace grip and shovel handle that jeopardized the prophylactic benefits of the current brace design. This limitation was not as evident when considering kinematics and sEMG data independently. Application A neuromechanical model (joint rotational stiffness) enhanced our ability to evaluate the brace design relative to kinematic and sEMG parameter-based metrics alone.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".