Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications.

Authors:
Ienaga N; Takahata S; Terayama K; Enomoto D; Ishihara H and 2 more

Journal:
Occup Ther Int

Publication Year: 2022

DOI:
10.1155/2022/6952999

PMCID:
PMC9729024

PMID:
36531757

View on PubMed View on PMC View Publisher
Journal Information

Full Title: Occup Ther Int

Abbreviation: Occup Ther Int

Country: Unknown

Publisher: Unknown

Language: N/A

Publication Details

Subject Category: Physical and Rehabilitation Medicine

Available in Europe PMC: Yes

Available in PMC: Yes

PDF Available: No

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Transparency Score
5/6
83.3% Transparent
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Core Indicators
Evidence found in paper:

"data availability the codes for calculating each proposed index (spb and ag) from a video file (mp4) are available in the github repository ( https://github com/decobocollabo/postural-control-assessment )."

Evidence found in paper:

"data availability the codes for calculating each proposed index (spb and ag) from a video file (mp4) are available in the github repository ( https://github com/decobocollabo/postural-control-assessment )."

Evidence found in paper:

"Conflicts of Interest The authors declare that there is no conflict of interest regarding the publication of this article."

Evidence found in paper:

"We are grateful to our study participants. This research was supported by the JSPS KAKENHI grant number JP21K12167."

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Open Access
Paper is freely available to read
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Assessment Info

Tool: rtransparent

OST Version: N/A

Last Updated: Aug 05, 2025