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Record W563527353

Contribution of visual, vestibular, and somatosensory systems to gait termination strategy

2005· book· en· W563527353 on OpenAlexaff
Zinat Shafaei-Shirazi

Bibliographic record

VenueUMI eBooks · 2005
Typebook
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVestibular systemSomatosensory systemSensory systemFeed forwardPhysical medicine and rehabilitationProprioceptionComputer sciencePsychologyNeuroscienceMedicineEngineeringControl engineering
DOInot available

Abstract

fetched live from OpenAlex

During human locomotion, availability of the online information about the environment and the body limbs (relative to each other and to the environment) is an important issue for the central nervous system (CNS) to modulate the motion strategies to control and maintain the postural balance. There are three main feedback systems, which update the CNS about the environmental and the body position relative to the surrounding space during performing any task, such as Gait Termination (GT). Those controlling systems are: visual, vestibular, and somatosensory systems (Dietz, 1992, Patla, 2003). Visual system contributes in feedforward control as an anticipatory strategy for dynamic stability for navigation in the environment (Patla, 2003). In reactive control, however, the information from the visual system is not fast enough to recover the body equilibrium from an unexpected perturbation such as slip (Patla, 2003). The vestibular system, detect the perturbation by head acceleration and activate extensor synergies to support the body. Moreover, the somatosensory information from the foot mechanoreceptors and load sensitive afferents in the joints provide feedback from the perturbation and send signal to initiate a polysynaptic response (Patla 2003; Oates et al., 2005). The focus of this study was to have a better understanding about the role of each of the three sensory systems in postural control during GT strategy. METHODS

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.350
Teacher spread0.326 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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