Evidence map›Paper›PMID 38982221›Full record

ArticleScientific reports2024

Performance of Microsoft Azure Kinect DK as a tool for estimating human body segment lengths.

Shiou-An Wang, Ming-Hua Lu, Ai-Teng Lee, Chao-Yu Chen, Li-Wen Lee

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 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

5 authors.

Shiou-An WangDepartment of Computer Science and Information Engineering, Hungkuo Delin University of Technology, New Taipei City, 236302, Taiwan.
Ming-Hua LuDepartment of Computer Science and Information Engineering, Hungkuo Delin University of Technology, New Taipei City, 236302, Taiwan.
Ai-Teng LeeDepartment of Diagnostic Radiology, Chang Gung Memorial Hospital, No. 8, Section West, Jiapu Road, Puzi City, Chiayi, 613016, Taiwan.
Chao-Yu ChenDepartment of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Chiayi, 613016, Taiwan.
Li-Wen LeeDepartment of Diagnostic Radiology, Chang Gung Memorial Hospital, No. 8, Section West, Jiapu Road, Puzi City, Chiayi, 613016, Taiwan. leeleeliwen@gmail.com.

Funding

Chang Gung Medical Foundation CMRPG6M0153National Science and technology Council of Taiwan NSTC 110-2314-B-182-064
6 · The paper itself

Abstract

The Microsoft Kinect depth sensor, with its built-in software that automatically captures joint coordinates without markers, could be a potential tool for ergonomic studies. This study investigates the performance of Kinect in limb segment lengths using dual-energy X-ray absorptiometry (DXA) as a reference. Healthy children and adults (n = 76) were recruited for limb length measurements by Kinect and DXA. The results showed consistent ratios of arm, forearm, thigh, and leg lengths to height, which were 0.16, 0.14, 0.23, and 0.22 respectively, for both age groups and methods. Kinect exhibited perfect correlation among all limb lengths, indicating fixed proportions assumed by its algorithm. Comparing the two methods, there was a strong correlation (R = 0.850-0.985) and good to excellent agreement (ICC = 0.829-0.977), except for the right leg in adults, where agreement was slightly lower but still moderate (ICC = 0.712). The measurement bias between the methods ranged from - 1.455 to 0.536 cm. In conclusion, Kinect yields outcomes similar to DXA, indicating its potential utility as a tool for ergonomic studies. However, the built-in algorithm of Kinect assumes fixed limb proportions for individuals, which may not be ideal for studies focusing on investigating limb discrepancies or anatomical differences.

Indexed as

Absorptiometry, PhotonAdolescentAdultAlgorithmsAnthropometryChildFemaleHumansMaleMiddle AgedSoftwareYoung Adult

Identifiers

PMID38982221
PMCPMC11233572

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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.