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Record W2772688672 · doi:10.1109/smc.2017.8122953

Closed-loop control of standing neuroprosthesis using PID controller

2017· article· en· W2772688672 on OpenAlexaff
Hossein Rouhani, Michael Same, Kei Masani, Ya Qi Li, Miloš R. Popović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsNeuroprostheticsPID controllerControl theory (sociology)Controller (irrigation)Closed loopControl engineeringComputer scienceControl (management)Control systemEngineeringTemperature controlPhysical medicine and rehabilitationArtificial intelligenceElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

Functional electrical stimulation (FES), applied to paralyzed or paretic muscles, can facilitate arm-free standing in patients with neurological disorders. At the same time, it has been shown that the able-bodied central nervous system regulates standing balance using a control strategy similar to proportional-integral-derivative (PID) control. The objective of this study was to investigate the capability of a PID controller for regulating an FES system applied to the ankle flexors for maintaining standing balance. We compared the performance of this controller to voluntary balance control of able-bodied individuals with compromised visual, vestibular and proprioceptive senses. According to our experimental results, the proposed PID control strategy outperformed the disrupted voluntary control of the ankle flexors while it did not require larger control efforts, in terms of the ankle torque. Therefore, this control strategy could be considered in for use in neurological patients, including spinal cord injured individuals, after further validations in the future.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0010.000
Open science0.0010.000
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 head0.023
GPT teacher head0.240
Teacher spread0.218 · 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
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

Citations3
Published2017
Admission routes1
Has abstractyes

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