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Dielectric elastomers as actuators for upper limb prosthetics: Challenges and opportunities

2007· review· en· W1972904005 on OpenAlexafffund
Elaine Biddiss, Tom Chau

Bibliographic record

VenueMedical Engineering & Physics · 2007
Typereview
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDurabilityActuatorElastomerComputer scienceMechanical engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Recent research has indicated that consumers of upper limb prostheses desire lighter-weight, anthropomorphic devices. The potential of dielectric elastomer (DE) actuators to better meet the design priorities of prosthesis users is explored. Current challenges are critically reviewed with respect to (1) durability, (2) precision control, (3) energy consumption, and (4) anthropomorphic implementation. The key points arising from the literature review are illustrated with empirical examples of the strain performance and durability of one of the most popular DEs, VHB 4910. Practical application of DE actuators in powered upper extremity prosthetics is at present impeded by poor durability and susceptibility to air-borne contaminants, unreliable control owing to viscoelasticity, hysteresis, stress relaxation and creep mechanisms, high voltage requirements, and insufficient stress and strain performance within the confines of anthropomorphic size, weight, and function. Our review suggests that the implementation of DE actuators in powered upper extremity prosthetics is not feasible at present but worthy of reevaluation as the materials advance.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.050
GPT teacher head0.282
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations140
Published2007
Admission routes2
Has abstractyes

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