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Record W4416995550 · doi:10.1186/s40691-025-00445-8

Development of a soft wearable robotic garment with fabric-based pneumatic artificial muscles for muscle support and postural alignment

2025· article· en· W4416995550 on OpenAlexaff
Sumin Helen Koo, Hyeon-seon Cho, J S Song, Gayeon Lee, Yumin Cho, Shinwon Chang, Yeong Jin Choi, Yeojin Claire Kim, Jiwon Chung, Yong‐Lae Park

Bibliographic record

VenueFashion and Textiles · 2025
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of British Columbia
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsActuatorArtificial muscleWearable computerElectromyographyPneumatic actuatorLumbarErector spinae musclesPneumatic artificial muscles

Abstract

fetched live from OpenAlex

Abstract A soft wearable robotic garment was developed to support lumbar muscle function and postural alignment by using cell-structured fabric pneumatic artificial muscles (cfPAMs). The actuator was constructed from two types of lightweight thermoplastic polyurethane-coated nylon fabrics and was designed with a flexible structure to enhance the contraction and force output. The performance of three actuator configurations (3-cell, 6-cell, and 8-cell) was evaluated in terms of contraction ratio, force generation, and response time. The 8-cell actuator exhibited the highest performance across these metrics. The actuator was integrated into a garment and tested in a user study involving 22 female participants. Surface electromyography of the lumbar erector spinae indicated a reduction in muscle activity during trunk flexion with actuator inflation relative to noninflated and control conditions. A trend toward a reduced full-body tilt was observed, and the user satisfaction was high, particularly for safety and functionality. These findings underscore the potential of cfPAM-based garments for daily musculoskeletal support.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.223
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations5
Published2025
Admission routes1
Has abstractyes

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