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Record W4408091198 · doi:10.1016/j.apergo.2025.104491

Effects of DC-powered pistol grip tool location and orientation on operator upper extremity stiffness and damping

2025· article· en· W4408091198 on OpenAlexaff
Rajalakshmi Arjun, Naveen Chandrashekar

Bibliographic record

VenueApplied Ergonomics · 2025
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStiffnessOrientation (vector space)Operator (biology)Structural engineeringPhysical medicine and rehabilitationEngineeringComputer scienceAcousticsMedicinePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

In automotive assembly lines, workers routinely used DC-powered pistol grip tools for the installation of threaded fasteners. The stiffness and damping offered by the hand-tool system dictates the handle displacement due to the reaction torque. The aim of the study was to predict the typical ranges of stiffness and damping offered by the upper extremity in different wrist orientations and locations while operating a pistol-grip hand tool. The hand-tool system was represented using a single degree-of-freedom torsional model and a deterministic approach was adopted to identify the system parameters. Tightening tasks were executed by ten experienced hand-tool operators at three torque levels (5 Nm, 7.5 Nm, and 10 Nm) and at four different fastener locations corresponding to varying wrist orientations. At 5 Nm, 7.5 Nm, and 10 Nm torques, the mean operator stiffnesses were 645 N/m, 879.5 N/m, and 1019 N/m respectively with a mean damping being 22.88 N/m, 15.14 N/m and 12.38 N/m respectively. The stiffness coefficients were different between wrist positions but not the damping coefficients. The research demonstrates the approach to model pistol grip hand tool operation and determine the stiffness and damping parameters. This approach could be used for determining optimal torque ranges and positions to minimize rotary tool handle displacement due to reaction torque, thereby reducing the risk of injury.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.256
Teacher spread0.250 · 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 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
Published2025
Admission routes1
Has abstractyes

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