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Record W2803531282 · doi:10.1016/j.jsams.2018.05.016

Consensus paper on testing and evaluation of military exoskeletons for the dismounted combatant

2018· article· en· W2803531282 on OpenAlex
Kurt L. Mudie, Angela Boynton, Thomas Karakolis, Meghan P. O’Donovan, Gregory B. Kanagaki, Harrison P. Crowell, Rezaul Begg, Michael E. LaFiandra, Daniel C. Billing

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of science and medicine in sport · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCombatantExoskeletonContext (archaeology)EngineeringUsabilityComputer scienceMilitary personnelSystems engineeringSimulationHuman–computer interaction

Abstract

fetched live from OpenAlex

Enhancing the capabilities of the dismounted combatant has been an enduring goal of international military research communities. Emerging developments in exoskeleton technology offers the potential to augment the dismounted combatant's capabilities. However, the ability to determine the value proposition of an exoskeleton in a military context is difficult due to the variety of methods and metrics used to evaluate previous devices. The aim of this paper was to present a standard framework for the evaluation and assessment of exoskeletons for use in the military. A structured and systematic methodology was developed from the end-user perspective and progresses from controlled laboratory conditions (Stage A), to simulated movements specific to the dismounted combatant (Stage B), and real-world military specific tasks (Stage C). A standard set of objective and subjective metrics were described to ensure a holistic assessment on the human response to wearing the exoskeleton and the device's mechanical performance during each stage. A standardised methodology will ensure further advancement of exoskeleton technology and support improved international collaboration across research and industry groups. In doing so, this better enables international military groups to evaluate a system's potential, with the hope of accelerating the maturity and ultimately the fielding of devices to augment the dismounted close combatant and small team capability.

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.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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