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Record W4413484420 · doi:10.1038/s41598-025-97757-y

Non-contact, non-visual, multi-person hallway gait monitoring

2025· article· en· W4413484420 on OpenAlexafffund
Hajar Abedi, Ahmad Ansariyan, Eric T. Hedge, Carmelo Mastrandrea, Plinio Pelegrini Morita, Jennifer Boger, Alexander Wong, Richard L. Hughson, George Shaker

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEngineering
TopicGait Recognition and Analysis
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersCanadian Institutes of Health ResearchCanadian Frailty NetworkCanadian Space AgencyMcGill University Health Centre
KeywordsComputer scienceGaitArtificial intelligenceComputer visionHuman–computer interactionPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

This paper presents a multi-person gait monitoring system designed for efficient operation in cluttered environments. The system demonstrates robust capabilities in tracking multiple closely spaced individuals and accurately extracting the walking speed, even in the presence of others. We address two significant challenges, including enhancing radar resolution and mitigating multipath effects in cluttered settings. Our method shows remarkable accuracy, with a maximum error of 0.33 m/s and a minimum of 0.005 m/s, as validated through 25 walking tests in a bedrest study. Its adaptability makes it a valuable clinical tool, offering insights for predicting underlying health issues in older adults.

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 score0.177
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.264
Teacher spread0.248 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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