Evaluation of Functional Tests Performance Using a Camera-based and Machine Learning Approach

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11510/23:10472238 RIV/68407700:21730/23:00369630

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of Functional Tests Performance Using a Camera-based and Machine Learning Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Evaluation of Functional Tests Performance Using a Camera-based and Machine Learning Approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/TM03000048" target="_blank" >TM03000048: Inteligentní systém služeb na podporu zdraví</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PLoS ONE

ISSN

1932-6203

e-ISSN

1932-6203

Svazek periodika

2023

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001098807300015

EID výsledku v databázi Scopus

2-s2.0-85175968582