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Record W2594919953 · doi:10.1109/tnsre.2017.2679722

Ankle Joint Intrinsic Dynamics is More Complex than a Mass-Spring-Damper Model

2017· article· en· W2594919953 on OpenAlexaff
Ehsan Sobhani Tehrani, Kian Jalaleddini, Robert E. Kearney

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMcGill University
FundersQatar National Research Fund
KeywordsAnkleJoint stiffnessStiffnessControl theory (sociology)TorqueJoint (building)Computer scienceMathematicsMechanicsPhysical medicine and rehabilitationStructural engineeringPhysicsMedicineEngineeringArtificial intelligenceAnatomy

Abstract

fetched live from OpenAlex

This paper describes a new small signal parametric model of ankle joint intrinsic mechanics in normal subjects. We found that intrinsic ankle mechanics is a third-order system and the second-order mass-spring-damper model, referred to as IBK, used by many researchers in the literature cannot adequately represent ankle dynamics at all frequencies in a number of important tasks. This was demonstrated using experimental data from five healthy subjects with no voluntary muscle contraction and at seven ankle positions covering the range of motion. We showed that the difference between the new third-order model and the conventional IBK model increased from dorsi to plantarflexed position. The new model was obtained using a multi-step identification procedure applied to experimental input/output data of the ankle joint. The procedure first identifies a non-parametric model of intrinsic joint stiffness where ankle position is the input and torque is the output. Then, in several steps, the model is converted into a continuous-time transfer function of ankle compliance, which is the inverse of stiffness. Finally, we showed that the third-order model is indeed structurally consistent with agonist-antagonist musculoskeletal structure of human ankle, which is not the case for the IBK model.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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.

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

Citations40
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Neural Systems and Rehabilitation EngineeringSame topicMuscle activation and electromyography studiesFrench-language works237,207