Evidence map›Paper›PMID 42228716›Full record

ArticlePloS one2026

A low-cost markerless motion capture system to automate functional gait assessment: Feasibility study.

Osman Darici, Chanel Cabak, Jeremy D Wong

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Osman DariciFaculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Chanel CabakFaculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Jeremy D WongFaculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.ORCID https://orcid.org/0000-0001-5564-1794

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional gait assessments in older adults have traditionally required manual in-person quantification of clinical measures such as walking speed and step placement. This reliance on individuals trained in motion analysis hinders the frequency with which they are performed and reduces their generalizability and replicability. To standardize, simplify, and broaden access to gait assessments we here deploy recently-developed open-source tools to produce a low-cost, AI driven markerless motion capture system with custom analysis software for Functional Gait Assessment. Our system uses 3 Cameras and was validated with a traditional marker-based system on subjects (N = 3), showing strong correlation to laboratory-standard measures of step length (R2 = 0.98), step width (R2 = 0.97), and head speed (R2 = 0.95). The markerless system's FGA reports demonstrated data similar to previous FGA findings in older adult subjects (N = 5). Moreover, supplemental standard biomechanical gait measures Step Width, walking Duration, and continuous Gait Speed may be integrated to augment existing FGA. This study demonstrates a proof-of-principle open-source markerless system for analyses of functional gait.

Indexed as

Gait AnalysisMotion CaptureAgedBiomechanical PhenomenaFeasibility StudiesFemaleGaitHumansMaleSoftwareWalking

Identifiers

PMID42228716
PMCPMC13229360

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.