Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context

dc.contributor.authorPalucci Vieira, Luiz H. [UNESP]
dc.contributor.authorSantiago, Paulo R P
dc.contributor.authorPinto, Allan
dc.contributor.authorAquino, Rodrigo
dc.contributor.authorTorres, Ricardo da S
dc.contributor.authorBarbieri, Fabio A. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionNTNU-Norwegian University of Science and Technology
dc.date.accessioned2022-05-01T15:13:30Z
dc.date.available2022-05-01T15:13:30Z
dc.date.issued2022-01-21
dc.description.abstractKicking is a fundamental skill in soccer that often contributes to match outcomes. Lower limb movement features (e.g., joint position and velocity) are determinants of kick performance. However, obtaining kicking kinematics under field conditions generally requires time-consuming manual tracking. The current study aimed to compare a contemporary markerless automatic motion estimation algorithm (OpenPose) with manual digitisation (DVIDEOW software) in obtaining on-field kicking kinematic parameters. An experimental dataset of under-17 players from all outfield positions was used. Kick attempts were performed in an official pitch against a goalkeeper. Four digital video cameras were used to record full-body motion during support and ball contact phases of each kick. Three-dimensional positions of hip, knee, ankle, toe and foot centre-of-mass (CMfoot) generally showed no significant differences when computed by automatic as compared to manual tracking (whole kicking movement cycle), while only z-coordinates of knee and calcaneus markers at specific points differed between methods. The resulting time-series matrices of positions (r2 = 0.94) and velocity signals (r2 = 0.68) were largely associated (all p < 0.01). The mean absolute error of OpenPose motion tracking was 3.49 cm for determining positions (ranging from 2.78 cm (CMfoot) to 4.13 cm (dominant hip)) and 1.29 m/s for calculating joint velocity (0.95 m/s (knee) to 1.50 m/s (non-dominant hip)) as compared to reference measures by manual digitisation. Angular range-of-motion showed significant correlations between methods for the ankle (r = 0.59, p < 0.01, large) and knee joint displacements (r = 0.84, p < 0.001, very large) but not in the hip (r = 0.04, p = 0.85, unclear). Markerless motion tracking (OpenPose) can help to successfully obtain some lower limb position, velocity, and joint angular outputs during kicks performed in a naturally occurring environment.en
dc.description.affiliationHuman Movement Research Laboratory (MOVI-LAB) Graduate Program in Movement Sciences Department of Physical Education Faculty of Sciences São Paulo State University (Unesp)
dc.description.affiliationLaBioCoM Biomechanics and Motor Control Laboratory EEFERP School of Physical Education and Sport of Ribeirão Preto USP University of São Paulo Campus Ribeirão Preto
dc.description.affiliationReasoning for Complex Data Laboratory (RECOD Lab) Institute of Computing University of Campinas
dc.description.affiliationFMRP Faculty of Medicine at Ribeirão Preto University of São Paulo
dc.description.affiliationLabSport, Department of Sports, CEFD Center of Physical Education and Sports, UFES Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil
dc.description.affiliationDepartment of ICT and Natural Sciences NTNU-Norwegian University of Science and Technology
dc.description.affiliationUnespHuman Movement Research Laboratory (MOVI-LAB) Graduate Program in Movement Sciences Department of Physical Education Faculty of Sciences São Paulo State University (Unesp)
dc.identifierhttp://dx.doi.org/10.3390/ijerph19031179
dc.identifier.citationInternational journal of environmental research and public health, v. 19, n. 3, 2022.
dc.identifier.doi10.3390/ijerph19031179
dc.identifier.issn1660-4601
dc.identifier.scopus2-s2.0-85125591290
dc.identifier.urihttp://hdl.handle.net/11449/234216
dc.language.isoeng
dc.relation.ispartofInternational journal of environmental research and public health
dc.sourceScopus
dc.subjectCOCO
dc.subjectdeep learning
dc.subjecthuman estimation
dc.subjectimage processing
dc.subjectMPII
dc.subjectteam sports
dc.titleAutomatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Contexten
dc.typeArtigo
unesp.author.orcid0000-0001-6981-756X[1]
unesp.author.orcid0000-0002-9460-8847[2]
unesp.author.orcid0000-0003-3765-8300[3]
unesp.author.orcid0000-0002-4885-7316 0000-0002-4885-7316 0000-0002-4885-7316[4]
unesp.author.orcid0000-0001-9772-263X[5]
unesp.author.orcid0000-0002-3678-8456[6]
unesp.departmentEducação Física - FCpt

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