Posture and Helmet Configuration Effects on Joint Reaction Loads in the Middle Cervical Spine
Bibliographic record
Abstract
INTRODUCTION: Between 43 and 97% of helicopter pilots in the Canadian Armed Forces report neck pain. Potential contributing factors include the weight of their helmet, night vision goggles (NVG), and counterweight (CW) combined with deviated neck postures. Therefore, the purpose of this investigation was to quantify changes in neck loads associated with posture, helmet, NVG, and CW. METHODS: Eight male subjects volunteered. They undertook one of five deviated neck postures (flexion, extension, lateral bending, axial rotation) times four configurations (no helmet, helmet only, helmet and NVG, and helmet, NVG, and CW). 3D kinematics and EMG from 10 muscles (5 bilaterally) drove a 3D inverse dynamics, EMG-driven model of the cervical spine which calculated joint compression and shear at C5-C6. RESULTS: The compression in the neutral posture was 116.5 (5.7) N, which increased to 143.7 (11.4) N due to a 12.7 N helmet. NVGs, weighing 7.9 N, also generated this disproportionate increase, where the compression was 164.2 (3.7) N. In flexion or extension, the compression increased with increasing head-supported mass, with a maximum of 315.8 (67.5) N with the CW in flexion. Anteroposterior shear was highest in the lateral bending [34.0 (6.2) N] condition, but was generally low (< 30 N). Mediolateral shear was less than 5 N for all conditions. DISCUSSION: Repositioning the center of gravity of the helmet with either NVGs or CW resulted in posture-specific changes to loading. Posture demonstrated a greater potential to reposition the head segment’s center of gravity compared to the helmet design. Therefore, helmet designs which consider repositioning the center of gravity may reduce loads in one posture, but likely exacerbate loading in other postures. Barrett JM, McKinnon CD, Dickerson CR, Laing AC, Callaghan JP. Posture and helmet configuration effects on joint reaction loads in the middle cervical spine. Aerosp Med Hum Perform. 2022; 93(5):458–466.
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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 | 0.001 |
| 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) | 0.002 | 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".