Evidence map›Paper›PMID 36701322›Full record

ArticlePloS one2023

Accuracy and repeatability of the Microsoft Azure Kinect for clinical measurement of motor function.

Johannes Bertram, Theresa Krüger, Hanna Marie Röhling, Ante Jelusic, Sebastian Mansow-Model, Roman Schniepp, Max Wuehr, Karen Otte

Registry-linked trialAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06512649 (Early Recognition and Intervention in Siblings at High-risk for Neurodevelopmental Disorder), which is not on this map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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.

NCT06512649 naunknown statusnot on this map

Early Recognition and Intervention in Siblings at High-risk for Neurodevelopmental Disorder

TypeinterventionalSponsorFondazione Don Carlo Gnocchi ETSRan2023 to 2026Enrolled48ConditionsNeurodevelopmental Disorders, Autism Spectrum DisorderArmsEarly intervention, Active monitoring
3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

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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

8 authors.

Johannes BertramGerman Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.ORCID 0000-0002-7787-2574
Theresa KrügerMotognosis GmbH, Berlin, Germany.
Hanna Marie RöhlingMotognosis GmbH, Berlin, Germany.
Ante JelusicGerman Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.
Sebastian Mansow-ModelMotognosis GmbH, Berlin, Germany.
Roman SchnieppGerman Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.
Max WuehrGerman Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University, Munich, Germany.
Karen OtteMotognosis GmbH, Berlin, Germany.ORCID 0000-0002-3518-5274

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative assessment of motor function is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric disorders of balance and gait. Its broad application, however, demands for low-cost and easy to use solutions that facilitate high-quality assessment outside laboratory settings. In this study, we validated in 30 healthy adults (12 female, age: 32.5 [22 - 62] years) the performance and accuracy of the latest generation of the Microsoft RGB-D camera, i.e., Azure Kinect (AK), in tracking body motion and providing estimates of clinical measures that characterise static posture, postural transitions, and locomotor function. The accuracy and repeatability of AK recordings was validated with a clinical reference standard multi-camera motion capture system (Qualisys) and compared to its predecessor Kinect version 2 (K2). Motion signal quality was evaluated by Pearson's correlation and signal-to-noise ratios while the accuracy of estimated clinical parameters was described by absolute and relative agreement based on intraclass correlation coefficients. The accuracy of AK-based body motion signals was moderate to excellent (RMSE 89 to 20 mm) and depended on the dimension of motion (highest for anterior-posterior dimension), the body region (highest for wrists and elbows, lowest for ankles and feet), and the specific motor task (highest for stand up and sit down, lowest for quiet standing). Most derived clinical parameters showed good to excellent accuracy (r .84 to .99) and repeatability (ICC(1,1) .55 to .94). The overall performance and limitations of body tracking by AK were comparable to its predecessor K2 in a cohort of young healthy adults. The observed accuracy and repeatability of AK-based evaluation of motor function indicate the potential for a broad application of high-quality and long-term monitoring of balance and gait in different non-specialised environments such as medical practices, nursing homes or community centres.

Indexed as

Elbow JointSoftwareAdultAgedBiomechanical PhenomenaFemaleGaitHumansMotionPostureReproducibility of Results

Identifiers

PMID36701322
PMCPMC9879399

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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.