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Record W2050018183 · doi:10.7205/milmed-d-09-00195

Case Study: A Novel Biomechanical Approach for Evaluating Extended Body Armor Systems

2010· article· en· W2050018183 on OpenAlexaff
Jessica C. Selinger, Chad E. Gooyers, Joan M. Stevenson, Patrick A. Costigan, Gabrielle S. Chafe

Bibliographic record

VenueMilitary Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsDefence Research and Development CanadaQueen's University
Fundersnot available
KeywordsArmourTrunkElectromyographyPhysical medicine and rehabilitationRange of motionMotion captureSimulationMotion (physics)Computer scienceMedicinePhysical therapyArtificial intelligenceMaterials science

Abstract

fetched live from OpenAlex

To combat the devastating effects of improvised explosive devices (IEDs), body armor that provides extended coverage has been developed. However, this extended coverage increases the armor's weight and may restrict movement. Throughout this case study, a novel technique to assess several armor systems was investigated. Four soldiers performed shoulder and trunk movements while wearing each of the six different armor inserts. Electromyography (EMG) was used to quantify muscular activity and inertial motion sensors were used to determine joint range of motion (ROM). Outcome measures included maximum ROM, integrated EMG, and the soldiers' subjective rankings. For the shoulder tasks, objective ROM and EMG measures were related to each other as well as to subjective rankings and armor material properties. Conversely, little agreement was found between measures for the trunk tasks. Results of this preliminary investigation indicate that combining shoulder ROM and EMG measures has the potential to provide an objective assessment of body armor systems.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.241
GPT teacher head0.524
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2010
Admission routes1
Has abstractyes

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