Evidence map›Paper›PMID 39218601›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Lower limb joint contact forces and ground reaction forces analysis based on Azure Kinect motion capture].

Yinghu Peng, Lin Wang, Zhenxian Chen, Xiaodong Dang, Fei Chen, Guanglin Li

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yinghu PengCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China.
Lin WangCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China.
Zhenxian ChenSchool of Mechanical Engineering, Chang'an University, Xi'an 710064, P. R. China.
Xiaodong DangSchool of Mechanical Engineering, Chang'an University, Xi'an 710064, P. R. China.
Fei ChenDepartment of Biomedical Engineering, Faculty of Engineering, Hong Kong Polytechnic University, Hong Kong SAR, 999077, P. R. China.
Guanglin LiCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean

Indexed as

Computer SimulationLower ExtremityBiomechanical PhenomenaGaitGait AnalysisHumansJointsKnee JointMotion CaptureSoftwareAzure Kinect motion captureGaitGround reaction forcesLower limb joint contact forcesMusculoskeletal multibody dynamics

Identifiers

PMID39218601
PMCPMC11366463

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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.