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Record W3021064703 · doi:10.1109/tla.2020.9082908

Eletronic Instrumentation in Lofstrand: Dynamic Force and Attitude Monitoring

2020· article· en· W3021064703 on OpenAlexaboutno aff
Jackson Paz B. De Souza, Danilo dos Santos Oliveira, Diogo de Oliveira Costa, Angélica de Oliveira Alves, José Henrique de Oliveira, Suélia de Siqueira Rodrigues Fleury Rosa

Bibliographic record

VenueIEEE Latin America Transactions · 2020
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCrutchAccelerometerGyroscopeModular designForce dynamicsComputer scienceInstrumentation (computer programming)Work (physics)Centripetal forceSimulationEngineeringControl theory (sociology)Artificial intelligencePhysicsAerospace engineeringMechanical engineeringStructural engineering

Abstract

fetched live from OpenAlex

This work provides quantitative data related to the use of Lofstrand crutches, these data are the strength applied on the armrest to the contact surface, the angle of the crutch relative to its axis of origin.Modular sensors of magnetometer, gyroscope and accelerometer were used. The results presented are: the dynamic force variation in the order of 0 kgf to 50 kgf was measured by the Force Sensing Resistor (FSR) sensor indicating the convergence of the data with the theoretical reference. Another innovative contribution of this work is the geoprocessing during the walk of the user. Finally, the purpose of the system is to explore the use of the Canadian crutch for the walking response and consequent storage of the users data, providing a mapping for user walk.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.006

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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