Comparing Video Analysis to Computerized Detection of Limb Position for the Diagnosis of Movement Control during Back Squat Exercise with Overload.

Autor:	Peres AB; Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), Piracicaba 13414-155, SP, Brazil.; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil., Sancassani A; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil., Castro EA; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.; Department of Physical Education, School of Sciences (FC), São Paulo State University (UNESP), Bauru 17033-360, SP, Brazil., Almeida TAF; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.; Department of Physical Education, School of Sciences (FC), São Paulo State University (UNESP), Bauru 17033-360, SP, Brazil., Massini DA; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.; Department of Physical Education, School of Sciences (FC), São Paulo State University (UNESP), Bauru 17033-360, SP, Brazil., Macedo AG; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.; Department of Physical Education, School of Sciences (FC), São Paulo State University (UNESP), Bauru 17033-360, SP, Brazil.; Pos-Graduation Program in Rehabilitation Sciences, Institute of Motricity Sciences, Federal University of Alfenas (UNIFAL), Alfenas 37133-840, MG, Brazil., Espada MC; Instituto Politécnico de Setúbal, Escola Superior de Educação, 2914-504 Setúbal, Portugal.; Sport Physical Activity and Health Research & INnovation CenTer (SPRINT), 2040-413 Rio Maior, Portugal.; Centre for the Study of Human Performance (CIPER), Faculdade de Motricidade Humana, Universidade de Lisboa, 1499-002 Cruz Quebrada, Portugal.; Comprehensive Health Research Centre (CHRC), Universidade de Évora, 7004-516 Évora, Portugal.; Life Quality Research Centre (CIEQV-Leiria), 2040-413 Rio Maior, Portugal., Hernández-Beltrán V; Training Optimization and Sports Performance Research Group (GOERD), Faculty of Sport Science, University of Extremadura, 10005 Cáceres, Spain., Gamonales JM; Training Optimization and Sports Performance Research Group (GOERD), Faculty of Sport Science, University of Extremadura, 10005 Cáceres, Spain.; Facultad Ciencias de la Salud, Universidad Francisco de Vitoria, 28223 Madrid, Spain.; Programa de Doctorado en Educación y Tecnología, Universidad a Distancia de Madrid, 28400 Madrid, Spain., Pessôa Filho DM; Graduate Programme in Human Development and Technologies, São Paulo State University (UNESP), Rio Claro 13506-900, SP, Brazil.; Department of Physical Education, School of Sciences (FC), São Paulo State University (UNESP), Bauru 17033-360, SP, Brazil.
Jazyk:	angličtina
Zdroj:	Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Mar 16; Vol. 24 (6). Date of Electronic Publication: 2024 Mar 16.
DOI:	10.3390/s24061910
Abstrakt:	Incorrect limb position while lifting heavy weights might compromise athlete success during weightlifting performance, similar to the way that it increases the risk of muscle injuries during resistance exercises, regardless of the individual's level of experience. However, practitioners might not have the necessary background knowledge for self-supervision of limb position and adjustment of the lifting position when improper movement occurs. Therefore, the computerized analysis of movement patterns might assist people in detecting changes in limb position during exercises with different loads or enhance the analysis of an observer with expertise in weightlifting exercises. In this study, hidden Markov models (HMMs) were employed to automate the detection of joint position and barbell trajectory during back squat exercises. Ten volunteers performed three lift movements each with a 0, 50, and 75% load based on body weight. A smartphone was used to record the movements in the sagittal plane, providing information for the analysis of variance and identifying significant position changes by video analysis ( p < 0.05). Data from individuals performing the same movements with no added weight load were used to train the HMMs to identify changes in the pattern. A comparison of HMMs and human experts revealed between 40% and 90% agreement, indicating the reliability of HMMs for identifying changes in the control of movements with added weight load. In addition, the results highlighted that HMMs can detect changes imperceptible to the human visual analysis.
Databáze:	MEDLINE
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