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Record W2115924629 · doi:10.1115/imece2008-66684

Effects of Mass Distribution and Configuration on the Energetic Losses at Impacts of Bipedal Walking Systems

2008· article· en· W2115924629 on OpenAlexaff
Josep M. Font-Llagunes, Jo ́zsef Ko ̈vecses

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Locomotion and Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsInverted pendulumWork (physics)BipedalismEnergeticsGaitComputer scienceEnergy consumptionControl theory (sociology)Motion (physics)Mechanical energyTopology (electrical circuits)SimulationPhysicsEngineeringPhysical medicine and rehabilitationControl (management)GeologyNonlinear systemArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Understanding the dynamics of human walking is a complex task due to the interaction of the musculoskeletal and the central nervous systems. Nevertheless, the use of simple models can provide useful insight into the mechanical aspects of bipedal locomotion. Such models exploit the observations that human walking significantly relies on passive dynamics and inverted pendulum-like behaviour. The mechanical analysis of walking involves the study of the finite motion single support phase and the impulsive motion of the impacts that occur at heel strike. Such impacts are dominant events because they represent a sudden topology transition and moreover, they are the main cause of energy consumption during the gait cycle. The aim of this work is to gain insight into the dynamics and energetics of heel strike. We use a concept that decouples the dynamics of the biped to the spaces of admissible and constrained motions at the topology transition. This approach is then applied to a straight-legged biped with upper body. Detailed analysis and discussions are presented to quantify the effects of the mass distribution and the impact configuration on the energetics of 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.164

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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 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

Citations2
Published2008
Admission routes1
Has abstractyes

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