MétaCan
Menu
Back to cohort
Record W2083471875 · doi:10.1016/s1350-4533(01)00126-6

Assessment of a three-dimensional robotic model for biomechanical-data acquisition of human movement

2002· article· en· W2083471875 on OpenAlexafffund
P. Desjardins, André Plamondon, M Gagnon

Bibliographic record

VenueMedical Engineering & Physics · 2002
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsLaurentian UniversityInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKinematicsData acquisitionSampling (signal processing)Computer scienceData samplingExperimental dataMovement (music)SimulationComputer visionArtificial intelligenceAcousticsStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

The use of a kinematic robotic model has not been implemented in the biomechanical-data acquisition protocol, as it has in workplace analysis, ergonomics and design. The purpose of this paper was to assess the use of a kinematic model to retrieve frames of human movements from data obtained at a low sampling frequency. From experimental trials with an original sampling frequency of 60 Hz, the data were sampled again at two lower frequencies, 5 Hz and 10 Hz. The model was then used to reconstitute the data to its original frequency (60 Hz). The results demonstrated that it was possible to retrieve a full 3-D human movement from a sampling rate lower than normal without sacrificing accuracy. It was observed from both reduced sampling frequencies that the error level was comparable to the usual accuracy of a DLT 3-D reconstruction technique. It was therefore concluded that the data retrieved from these two frequencies were very similar to the original data sampled at 60 Hz.

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: none
Teacher disagreement score0.847
Threshold uncertainty score0.355

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 head0.039
GPT teacher head0.311
Teacher spread0.272 · 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
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueMedical Engineering & PhysicsSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207