Evidence map›Paper›PMID 34526074›Full record

ArticleJournal of neuroengineering and rehabilitation2021

Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions.

Sina Mehdizadeh, Hoda Nabavi, Andrea Sabo, Twinkle Arora, Andrea Iaboni, Babak Taati

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05366114 (Assessing the Feasibility of a Smartphone-based, Machine Learning Visual Imaging Application for Assessment of Hyperextensibility of Peripheral Joints in Ehlers Danlos Syndrome), which is not on this map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
14.3field-weighted citation impact, top 1% 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.

NCT05366114 unknown statusnot on this mapstarted 2022, after this paper: background citation

Assessing the Feasibility of a Smartphone-based, Machine Learning Visual Imaging Application for Assessment of Hyperextensibility of Peripheral Joints in Ehlers Danlos Syndrome

TypeobservationalSponsorUniversity Health Network, TorontoRan2022 to 2024Enrolled225ConditionsEhlers-Danlos SyndromeArmsNo intervention, additional video data collection only
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 47 citations in OpenAlex.

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  11. Detecting the symptoms of Parkinson's disease with non-standard video.Journal of neuroengineering and rehabilitation · 2024
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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

6 authors at 2 institutions in 1 country.

Sina MehdizadehKITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.
Hoda NabaviKITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.
Andrea SaboKITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.
Twinkle AroraKITE- Toronto Rehabilitation Institute, University Health Network, 550 University Ave., Toronto, ON, M5G 2A2, Canada.
Andrea IaboniDepartment of Psychiatry, University of Toronto, Toronto, ON, Canada.
Babak TaatiInstitute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada. Babak.taati@uhn.ca.ORCID 0000-0001-9763-4293
University Health Network · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population.

methodsWe compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant's averaged gait variables.

resultsOur findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement.

conclusionsThere are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.

Indexed as

GaitWalkingAgedAged, 80 and overAlgorithmsBiomechanical PhenomenaHumansReproducibility of ResultsVideo RecordingDeep learningGaitHuman pose estimationWalking

Identifiers

PMID34526074
PMCPMC8443117
OpenAlexW3199234813

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.