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Record W2055073803 · doi:10.1080/00140130701237592

Effect of joystick stiffness, movement speed and movement direction on joystick and upper limb kinematics when using hydraulic-actuation joystick controls in heavy vehicles

2007· article· en· W2055073803 on OpenAlexafffund
Michele Oliver, Maureen Tingley, Robert J. Rogers, Jeremy Rickards, E. Biden

Bibliographic record

VenueErgonomics · 2007
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversity of New BrunswickUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJoystickKinematicsElbowSimulationComputer scienceEngineeringPhysicsAnatomyMedicine

Abstract

fetched live from OpenAlex

Despite the widespread use of hydraulic-actuation joysticks in mobile North American construction, mining and forestry vehicles, the biomechanical effects that joysticks have on their human operators has not been studied extensively. Using nine unskilled joystick operators and a laboratory mock-up with a commonly used North American heavy off-road equipment hydraulic-actuation joystick and operator seat, the purpose of this work was to quantify and compare the effects of three hydraulic-actuation joystick stiffnesses and two movement speeds on upper limb and joystick kinematics as one of the initial steps towards the development of a hydraulic-actuation joystick design protocol. In addition to providing a detailed description of the kinematics of a constrained occupational task, coupled with the corresponding effects of the task on operator upper limb kinematics, results from principal component analysis and ANOVA procedures revealed a number of differences in joystick and upper limb angle ranges and movement curve shapes resulting from the various joystick stiffness-speed combinations tested. For the most part, these joystick motion alterations were caused by small, insignificant changes in one or more upper limb joint angles. The two exceptions occurred for forward movements of the joystick; the fast speed - light stiffness condition movement pattern shape change was caused primarily by an alteration of the elbow flexion-extension movement pattern. Similarly, the fast speed - normal stiffness condition movement curve shape perturbation - was caused principally by a combination of significant movement curve shape alterations to elbow flexion-extension, external-internal shoulder rotation and flexion-extension of the shoulder. The finding that joystick stiffness and speed alterations affect joystick and upper limb kinematics minimally indicates that the joystick design approach of modelling the joystick and operator upper limb as a closed linkage system should be pursued. This approach would allow one to simulate the upper limb and joystick kinematics that result from virtual changes to upper limb and joystick lengths.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.286
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2007
Admission routes2
Has abstractyes

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