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Record W4410391618 · doi:10.61372/vvrj.v6i1.3030

Wheeled Robot for Human Performance Analysis on a Running Track

2024· article· en· W4410391618 on OpenAlexfundno aff
Amos Colocho, Riquel Owusu, Sabrina Smurro, Diego Martínez Soto, Stephen G. McGill

Bibliographic record

VenueVeritas Villanova Research Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsnot available
FundersMcGill University
KeywordsTrack (disk drive)Computer scienceRobotArtificial intelligenceAeronauticsHuman–computer interactionEngineeringOperating system

Abstract

fetched live from OpenAlex

This research project investigates runners' performance through innovative technology, aiming to foster a human-robot partnership. Our study involves the development of a wheeled, remote-controlled running track robot that will integrate these elements. The robot's capabilities were first modeled using simulations, with an emphasis on efficiency and high torque capabilities. The runner will wear a heart rate sensor during training on a 400m track, which will output biometric data to the running track robot. The robot is designed to analyze this data logged by the heart rate sensor. Future research will aim to develop the robot as a biomarker-driven pacing tool that can leverage the head-to-head psyche that runners experience throughout competition. This will allow the running track robot to act as a pacing robot that can analyze the biometric data in real time and provide personalized pacing 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.074
GPT teacher head0.371
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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