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Record W1993570376 · doi:10.1145/2407336.2407369

Static pose reconstruction with an instrumented bouldering wall

2012· article· en· W1993570376 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsMcGill University
Fundersnot available
KeywordsSolverCalibrationMotion captureContact forceComputer scienceTorqueProcess (computing)Set (abstract data type)Function (biology)SimulationMotion (physics)Artificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper describes the design and construction of an instrumented bouldering wall, and a technique for estimating poses by optimizing an objective function involving contact forces. We describe the design and calibration of the wall, which can capture the contact forces and torques during climbing while motion capture (MoCap) records the climber pose, and present a solution for identifying static poses for a given set of holds and forces. We show results of our calibration process and static poses estimated for different measured forces. To estimate poses from forces, we use optimization and start with an inexpensive objective to guide the solver toward the optimal solution. When good candidates are encountered, the full objective function is evaluated with a physics-based simulation to determine physical plausibility while meeting additional constraints. Comparison between our reconstructed poses and MoCap show that our objective function is a good model for human posture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.578

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 communication0.0000.001
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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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

Quick stats

Citations15
Published2012
Admission routes1
Has abstractyes

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