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Record W3090370924 · doi:10.3390/app10196841

Towards Management of Residual Limb Volume: Monitoring the Prosthetic Interface Pressure to Detect Volume Fluctuations—A Feasibility Study

2020· article· en· W3090370924 on OpenAlexaff
Chakaveh Ahmadizadeh, Brittany Pousett, Carlo Menon

Bibliographic record

VenueApplied Sciences · 2020
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsActivation LaboratoriesSimon Fraser University
Fundersnot available
KeywordsVolume (thermodynamics)Interface (matter)ProsthesisBiomedical engineeringResidualComputer scienceResidual volumeLower limbSimulationEngineeringMedicineSurgeryArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

(1) Motivation: Variations in the volume of the residual limb negatively impact various aspects of prosthesis use including the prosthetic socket fit. Although volume adjustment systems mitigate corresponding fit problems to some extent, some users still find the management of these systems challenging. With the ultimate goal of creating a feedback system that assists users with the management of their volume adjustment systems, this study demonstrates the feasibility of detecting variations in the volume of the residual limb. (2) Methods: Measurements of the interface force at the bottom of the prosthetic socket were used as indicators of variations in the volume of the residual limb. Force sensitive resistors (FSRs) were placed at the bottom of participants’ prosthetic sockets to monitor the interface limb–socket force as participants walked on a flat surface. Two phases of experiments were carried out: The first phase considered variations simulated by three prosthetic sock plies, established the feasibility of detecting variations in the volume of the limb based on the interface force, and further determined the locations at which the interface force could be used to detect variations in the limb’s volume. Having validated the effectiveness of the proposed method in the first phase, the second phase was carried out to determine the smallest detectable variation of the limb’s volume using the proposed method. In this phase, variations simulated by one and two prosthetic sock plies were considered. Four and three volunteers with transtibial amputations participated in the first and the second phases, respectively. (3) Results: Results of the first phase showed that an increase in the volume of the limb resulted in a decrease in the force measured at the distal location of the prosthetic sockets of all participants; however, the smallest detected variation could not be statistically confirmed.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.272
Teacher spread0.241 · 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 designObservational
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

Citations38
Published2020
Admission routes1
Has abstractyes

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