MétaCan
Menu
Back to cohort
Record W4295953631 · doi:10.4050/f-0078-2022-17499

An Evaluation of Human Performance with a Large Area Touchscreen in a Simulated Rotary Wing Environment

2022· article· en· W4295953631 on OpenAlexaff
Jason Browning, Kathryn A. Guy, Margaret Lampazzi, Catherine Daly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsTouchscreenCockpitUsabilityComputer scienceHuman–computer interactionGestureWingZoomSimulationInterface (matter)Task (project management)EngineeringAeronauticsComputer visionSystems engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents results of a human machine interface (HMI) evaluation that examined representative flight deck tasks with a large area touchscreen installed in a ride quality simulator that replicated rotary wing vibration profiles. Touchscreens have made their way onto the flight deck of many fixed-wing aircraft and recently into rotary-wing cockpits as well. As such, there is a need to better understand how task performance is impacted by the unique vibratory environment encountered in helicopters. A large area touchscreen (LAD) was evaluated by 14 pilots conducting various touch tasks (target selection, data entry, swiping, long press and zoom), under three varying levels of vibration with and without flight gloves. Performance was assessed objectively (time to completion, touch accuracy, error rates) and subjectively (usability, musculoskeletal discomfort, and video footage). Design recommendations are made for display interface design, including target size, data entry and use of gestures. This study was reviewed and approved by an independent Institutional Review Board for the protection of human subjects.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0070.000

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicErgonomics and Musculoskeletal DisordersFrench-language works237,207