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Record W2133630943 · doi:10.1109/robot.2001.932964

Feedforward and deterministic fuzzy control of balance and posture during human gait

2002· article· en· W2133630943 on OpenAlexaff
Eric Kubica, D. Wang, DA Winter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTorsoFeed forwardControl theory (sociology)Controller (irrigation)GaitComputer scienceFuzzy control systemBalance (ability)Process (computing)Control systemLinear-quadratic regulatorFeedforward neural networkControl engineeringControl (management)Fuzzy logicArtificial neural networkEngineeringPhysical medicine and rehabilitationArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The primary objective of this research is to model the biomechanical control system employed by the central nervous system (CNS) to maintain posture and balance of the head-arms-torso (HAT) during gait. More specifically, the intent is to stabilize a model of the upper body so that the HAT response is similar to that found experimentally in human subjects during gait. This is accomplished by using appropriate physiological parameters as feedback to generate realistic control signals at the hip musculature. The modelling includes the HAT musculoskeletal characteristics as well as pure neural time delays. The CNS control system is modelled by a linear state feedback controller as well as a hybrid fuzzy controller that has been adapted from a linear quadratic regulator. In addition, a newly derived feedforward component is demonstrated. It is expected that a better understanding of the human fait process will prove valuable in designing assistive devices and bipedal robots.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations15
Published2002
Admission routes1
Has abstractyes

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