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Record W4403189455 · doi:10.3390/app14198971

Skilled Workers’ Perspectives on Utilizing a Passive Shoulder Exoskeleton in Construction

2024· article· en· W4403189455 on OpenAlexafffund
Bronson Du, Kumara G. Somasundram, A. R. Johnston, Philip Bigelow, Mohammad Abdoli-Eramaki, K Jordan, Marcus Yung, Amin Yazdani

Bibliographic record

VenueApplied Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsToronto Metropolitan UniversityUniversity of WaterlooConestoga College
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsExoskeletonConstruction engineeringBusinessMedicinePhysical medicine and rehabilitationEngineering

Abstract

fetched live from OpenAlex

This field study explores construction workers’ perceptions of using a passive shoulder exoskeleton to better understand how to improve its adoption in construction. We provided forty-one construction workers with an exoskeleton to perform their regular work activities for two days. Workers’ feedback of the tool was collected at the end of each day. Two-thirds indicated they would likely or very likely use an exoskeleton if their employer provided it. Participants felt exoskeletons were helpful for specific overhead tasks, such as installing upper tracks, framing and drywalling bulkheads, taping and mudding ceilings, and installing light fixtures. To improve their adoption within the construction industry, exoskeletons should be designed to be compatible with harnesses and toolbelts, be close-fitting to allow working in tight spaces, be easily adjustable (for fit and level of support), be rugged and easy to clean, and should not encumber workers in performing their tasks.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.570
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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