Evaluation of Functional Tests Performance Using a Camera-based and Machine Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F23%3A00369630" target="_blank" >RIV/68407700:21460/23:00369630 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11510/23:10472238 RIV/68407700:21730/23:00369630
Result on the web
<a href="https://doi.org/10.1371/journal.pone.0288279" target="_blank" >https://doi.org/10.1371/journal.pone.0288279</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0288279" target="_blank" >10.1371/journal.pone.0288279</a>
Alternative languages
Result language
angličtina
Original language name
Evaluation of Functional Tests Performance Using a Camera-based and Machine Learning Approach
Original language description
The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists’ assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TM03000048" target="_blank" >TM03000048: Intelligent Health Promotion Service System</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLoS ONE
ISSN
1932-6203
e-ISSN
1932-6203
Volume of the periodical
2023
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1-15
UT code for WoS article
001098807300015
EID of the result in the Scopus database
2-s2.0-85175968582