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Record W2111205911 · doi:10.1115/1.4005725

Fluid-Power Harvesting by Under-Foot Bellows During Human Gait

2012· article· en· W2111205911 on OpenAlexfundno aff
Robin Chin, Elizabeth T. Hsiao‐Wecksler, Eric Loth

Bibliographic record

VenueJournal of Fluids Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsnot available
FundersMcMaster University
KeywordsBellowsGaitHeelGait cyclePower (physics)Compression (physics)Computer scienceMechanical engineeringEngineeringStructural engineeringMaterials sciencePhysical medicine and rehabilitationPhysicsMedicine

Abstract

fetched live from OpenAlex

Pneumatic and hydraulic bellows were investigated for under-foot power harvesting during human walking. Placement under the heel allows the bellow to be compressed during the heel strike of the gait cycle, whereas placement under the metatarsal allows compression during the mid-stance and toe-off phases. In either case, body weight is used as the power source for a self-contained fluid power circuit. Once unweighted, air is drawn into the bellow through a one-way valve allowing the bellow to recharge as it expands during the swing phase of the gait cycle. A collapsible spring was placed inside the bellow to ensure full opened conditions for this phase. To evaluate this concept, experimental studies were conducted on two circular bellows with outside diameters of 4.13 cm and 6.35 cm placed under the heel or the metatarsal of the foot, on a person walking on a treadmill. These pressure profiles were then reproduced on a compression testing machine to investigate the power generated per cycle. During normal walking, the pneumatic bellows generated peak power levels of 20–25 W and maximum pressures of 450 kPa. The average power available over a single cycle was 1.5 and 4.5 W for the small and large bellows, respectively. This novel use of bellows demonstrates the ability to use these devices for regenerative fluid power harvesting capabilities during walking.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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.

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
Published2012
Admission routes1
Has abstractyes

Explore more

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