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Record W4327590907 · doi:10.1002/aisy.202200370

Fluid‐Driven High‐Performance Bionic Artificial Muscle with Adjustable Muscle Architecture

2023· article· en· W4327590907 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Intelligent Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersInnovation and Technology Commission - Hong KongInnovation and Technology FundTianjin University
KeywordsMuscle architectureArtificial muscleSarcomereSkeletal muscleExoskeletonMyofilamentRoboticsComputer scienceArtificial intelligenceBiomedical engineeringActuatorRobotAnatomySimulationEngineeringMyocyteBiology

Abstract

fetched live from OpenAlex

High‐performance artificial muscle is always the pursuit of researchers for robotics. Herein, a bionic artificial muscle is reported called “ExoMuscle” mimicking the sarcomere in skeletal muscle with a bio‐inspired structure to contract “myofilaments” enabling the artificial muscle to mimic the architecture of muscle such as parallel, fusiform, convergent, and pennation and beyond the performance of skeletal muscle. The reported actuators excel in various aspects compared with skeletal muscle including actuation stress (0.41–0.9 MPa), strain (50%), optimal length, velocity‐independence output, power density (10.94 kW kg −1 ), and efficiency (69.11%). With its own adjustable pennation architecture, it achieves variable actuation stress up to 0.9 MPa meanwhile maintaining high efficiency. Furthermore, ExoMuscle highly conforms to the anatomical complexity of the human body to cooperate with skeletal muscles closely opening the door for bio‐robotics, especially wearable robots.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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