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Record W2092104053 · doi:10.1115/1.4028820

Development and Performance Evaluation of a Multi-PID Muscle Loading Driven In Vitro Active-Motion Shoulder Simulator and Application to Assessing Reverse Total Shoulder Arthroplasty

2014· article· en· W2092104053 on OpenAlexafffund
Joshua W. Giles, Louis M. Ferreira, George S. Athwal, James A. Johnson

Bibliographic record

VenueJournal of Biomechanical Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsHand and Upper Limb ClinicWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPID controllerKinematicsControl theory (sociology)SimulationSetpointStandard deviationBiomedical engineeringRange of motionMathematicsComputer scienceEngineeringSurgeryPhysicsMedicineControl engineering

Abstract

fetched live from OpenAlex

In vitro active shoulder motion simulation can provide improved understanding of shoulder biomechanics; however, accurate simulators using advanced control theory have not been developed. Therefore, our objective was to develop and evaluate a simulator which uses real-time kinematic feedback and closed loop proportional integral differential (PID) control to produce motion. The simulator's ability to investigate a clinically relevant variable-namely muscle loading changes resulting from reverse total shoulder arthroplasty (RTSA)-was evaluated and compared to previous findings to further demonstrate its efficacy. Motion control of cadaveric shoulders was achieved by applying continuously variable forces to seven muscle groups. Muscle forces controlling each of the three glenohumeral rotational degrees of freedom (DOF) were modulated using three independent PID controllers running in parallel, each using measured Euler angles as their process variable. Each PID controller was configured and tuned to control the loading of a set of muscles which, from previous in vivo investigations, were found to be primarily responsible for movement in the PID's DOF. The simulator's ability to follow setpoint profiles for abduction, axial rotation, and horizontal extension was assessed using root mean squared error (RMSE) and average standard deviation (ASD) for multiple levels of arm mass replacement. A specimen was then implanted with an RTSA, and the effect of joint lateralization (0, 5, 10 mm) on the total deltoid force required to produce motion was assessed. Maximum profiling error was <2.1 deg for abduction and 2.2 deg for horizontal extension with RMSE of <1 deg. The nonprofiled DOF were maintained to within 5.0 deg with RMSE <1.0 deg. Repeatability was high, with ASDs of <0.31 deg. RMSE and ASD were similar for all levels of arm mass replacement (0.73-1.04 and 0.14-0.22 deg). Lateralizing the joint's center of rotation (CoR) increased total deltoid force by up to 8.5% body weight with the maximum early in abduction. This simulator, which is the first to use closed loop control, accurately controls the shoulder's three rotational DOF with high repeatability, and produces results that are in agreement with previous investigations. This simulator's improved performance, in comparison to others, increases the statistical power of its findings and thus its ability to provide new biomechanical insights.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.314
Teacher spread0.286 · 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 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

Citations23
Published2014
Admission routes2
Has abstractyes

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