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Record W6989807176

Characterizing the biomechanical, physiological, and comfort impact of military torso-borne loads for female participants and manikin-based simulations

2022· dissertation· en· W6989807176 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTorsoRepeated measures designAthletesBiomechanicsBackpackWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Torso-borne equipment (TBE) such as body armour, load carriage vests, and rucksacks negatively affect individual biomechanics and physiology. Injury risk, discomfort, and performance decrements are increased in females. However, there is a lack of research investigating female load carriage using occupationally relevant equipment, loads, and tasks. Thus, the purpose of this thesis was to compare the efficacy of a reduced coverage armour system and full torso coverage armour system for females using occupationally relevant equipment, load, and tasks and to partially validate a female manikin designed to assess TBE. This work could help inform interventions such as conditioning strategies and future equipment design for female soldiers.
\nThe first portion of the thesis compared range of motion, march kinematics, shoulder and hip skin pressures on the march, perceived discomfort on the march, and time to traverse a wall in a baseline and two in-service TBE (i.e., Reduced Coverage (RC) and Full Torso Coverage (FTC)). Eight varsity-level athletes (20.1±1.7y, 171.4±5cm, 70.7±8.1kg) completed the tasks in a repeated measures study design. Linear mixed-effects model ANOVAs followed by Tukey’s post-hoc procedures when appropriate were performed (p<0.05). Results showed that the RC performed better or the same as the FTC for most outcome measures. Reducing torso coverage of TBE and altering bulk location could improve mobility performance and may mitigate injury risk for females as well as smaller males. 
\nThe second part of the study provided a preliminary validation for a newly commissioned female manikin designed by Queen’s University. The validation compared results between the female manikin and a physically active female (24y, 166.0cm, 66.3kg) both of whom were approximately the same size as 50th percentile females in the Canadian Armed Forces. For both the manikin and human, Clothe-the-Soldier equipment and pack were used during a march at a step frequency of 1.8 steps/s to collect and compare pack strap forces at five locations, shoulder and hip skin pressures, and body-payload relative motion. To look for differences between the manikin and human, mean values were compared coupled with visual analyses of average waveforms over a gait cycle. Two separate sensitivity analyses were performed to assess the effects of variable strap forces and lean angles on outcome measures. The human completed the march over four separate strap forces settings (i.e., 26N, 36N, 46N, 56N) where outcome measures were shoulder and hip skin pressures and body-payload relative motion. The manikin completed the march over four different lean angles (i.e., 6.7°, 10.6°, 14.5°, 18.4°) where outcome measures were pack strap forces at five locations, shoulder and hip skin pressures, and body-payload relative motion. It was found that manikin lean angles of 6.7-18.4° and human strap force between 35N and 54N had minimal effects on the outcome measures/conclusions when wearing the tested TBE. The manikin was able to replicate the human results for most outcome measures except for peak pressures at the shoulders and hips. Though more validation is required, initial results suggest that the manikin can be used for TBE assessment.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.397
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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