Evidence map›Paper›PMID 34695159›Full record

ArticlePloS one2021

Accuracy of Kinovea software in estimating body segment movements during falls captured on standard video: Effects of fall direction, camera perspective and video calibration technique.

Nataliya Shishov, Karam Elabd, Vicki Komisar, Helen Chong, Stephen N Robinovitch

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
8.2field-weighted citation impact, top 2% of its field
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

18 citing papers in PubMed, 35 citations in OpenAlex.

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  12. Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025
    Review
  13. Impact forces in backward falls: Subject-specific video-based rigid body simulation of backward falls.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2023
    Article
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  17. Observational
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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

5 authors at 2 institutions in 1 country.

Nataliya ShishovDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID 0000-0002-9148-1019
Karam ElabdSchool of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.
Vicki KomisarDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID 0000-0003-3504-1796
Helen ChongDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.
Stephen N RobinovitchDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada.ORCID 0000-0003-3881-6227
Simon Fraser University · CAUniversity of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Falls are a major cause of unintentional injuries. Understanding the movements of the body during falls is important to the design of fall prevention and management strategies, including exercise programs, mobility aids, fall detectors, protective gear, and safer environments. Video footage of real-life falls is increasingly available, and may be used with digitization software to extract kinematic features of falls. We examined the validity of this approach by conducting laboratory falling experiments, and comparing linear and angular positions and velocities measured from 3D motion capture to estimates from Kinovea 2D digitization software based on standard surveillance video (30 Hz, 640x480 pixels). We also examined how Kinovea accuracy depended on fall direction, camera angle, filtering cut-off frequency, and calibration technique. For a camera oriented perpendicular to the plane of the fall (90 degrees), Kinovea position data filtered at 10 Hz, and video calibration using a 2D grid, mean root mean square errors were 0.050 m or 9% of the signal amplitude and 0.22 m/s (7%) for vertical position and velocity, and 0.035 m (6%) and 0.16 m/s (7%) for horizontal position and velocity. Errors in angular measures averaged over 2-fold higher in sideways than forward or backward falls, due to out-of-plane movement of the knees and elbows. Errors in horizontal velocity were 2.5-fold higher for a 30 than 90 degree camera angle, and 1.6-fold higher for calibration using participants' height (1D) instead of a 2D grid. When compared to 10 Hz, filtering at 3 Hz caused velocity errors to increase 1.4-fold. Our results demonstrate that Kinovea can be applied to 30 Hz video to measure linear positions and velocities to within 9% accuracy. Lower accuracy was observed for angular kinematics of the upper and lower limb in sideways falls, and for horizontal measures from 30 degree cameras or 1D height-based calibration.

Indexed as

Accidental FallsSoftwareAdultBiomechanical PhenomenaFemaleHumansMaleMovementVideo RecordingYoung Adult

Identifiers

PMID34695159
PMCPMC8544843
OpenAlexW3209806666

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.