Skeleton Detection Using MediaPipe as a Tool for Musculoskeletal Disorders Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39922654" target="_blank" >RIV/00216275:25530/24:39922654 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-53549-9_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-53549-9_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-53549-9_4" target="_blank" >10.1007/978-3-031-53549-9_4</a>
Alternative languages
Result language
angličtina
Original language name
Skeleton Detection Using MediaPipe as a Tool for Musculoskeletal Disorders Analysis
Original language description
Skeleton detection, also known as human pose estimation (HPE), is becoming more and more popular as it can be applied in a range of applications such as game entertainment, human-machine interaction, VR-based projects, medical rehabilitation, etc. Thanks to the booming development of deep learning, HPE solutions can be implemented using deep learning methods which require standard 2D RGB images or video sequences as input. That is, technology nowadays is making HPE solutions more and more lightweight and fast which is possible to run on mobile devices for the daily use of skeleton detection. This article covers a brief survey of current deep learning-based human pose estimation approaches in the first place. Then, a lightweight deep learning model – MediaPipe – will be illustrated from all the perspectives of its structure, working flow, strengths & weaknesses and the more concerned compatibility in platforms and programming languages. As a result, a multi-platform application for collecting movement data from patients suffering from musculoskeletal diseases relying on MediaPipe is introduced. Finally, there is a summary of achievements and obstacles of application development, which is significant as it can be a signpost for teams who are doing or about to do an application based on the MediaPipe library.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Software Engineering Methods in Systems and Network Systems (CoMeSySo 2023)
ISBN
978-3-031-53548-2
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
15
Pages from-to
35-50
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Zlín
Event date
Apr 12, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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