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Record W2802024868 · doi:10.1139/tcsme-2016-0053

SWINGING LEG CONTROL OF A LOWER LIMB EXOSKELETON VIA A SHOE WITH IN-SOLE SENSING

2016· article· en· W2802024868 on OpenAlexvenueno aff
Yanhe Zhu, Chao Zhang, Jizhuang Fan, Hongying Yu, Jie Zhao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsExoskeletonSwingDeflection (physics)TorqueComputer scienceLower limbSimulationControl theory (sociology)EngineeringControl (management)Artificial intelligenceMechanical engineeringPhysicsMedicine

Abstract

fetched live from OpenAlex

A lower limb exoskeleton can help in weight-bearing and walking to assist laborers doing heavy work. For exoskeleton-assisted walking, the wearing comfort and walking convenience are important so there must be minimal interference with leg movement. Hence, a peculiar design strategy based on an in-sole sensing shoe is presented to achieve real-time motion detection and follow-up control of the moving leg. Compared to the elastic muscle extension, the sensor must exhibit minimal deflection under load. Therefore, an ultra-thin structure integrating 6 bar linkages and 3 cantilevers has been used in the design of the in-sole sensing shoe which can detect force in two directions and torque in one. A swing phase experiment and a random leg motion test were carried out. Results show validity of human motion detection and follow-up control strategy based on this plantar surface sensor.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.167
Teacher spread0.163 · 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 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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207