Machine learning-based classification of Taekwondo Poomsae side kick performance using kinematic parameters and physical characteristics.
To develop and validate machine learning (ML) models for classifying Taekwondo Poomsae side kick (SK) performance using kinematic parameters and physical function characteristics. Forty collegiate Taekwondo Poomsae athletes performed SKs with both legs. Two models were developed: a kinematic model i...
| Publicado en: | Sports Biomechanics Vol. 24; no. 12; pp. 3522 - 3542 |
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| Autores principales: | , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191459143&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191459143 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14763141 I7F jtl: Sports Biomechanics issn: 14763141 maglogo: N pubinfo: dt: Dec2025 vid: 24 iid: 12 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 191459143 186448027 191459143 191459143 10.1080/14763141.2025.2525557 191459143 ppf: 3522 ppct: 20 formats: tig: atl: Machine learning-based classification of Taekwondo Poomsae side kick performance using kinematic parameters and physical characteristics. aug: au: Hwang, Ui-Jae Jung, Sung-Hoon Ji, Ho-Chul Choi, Sil-Ah Bang, In-Ju affil: Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China sug: subj: Martial Arts Classification Martial Arts Physiology Kicking Physiology Athletic Performance Evaluation Physical Fitness Evaluation Kinematics Machine Learning Sociodemographic Factors Ankle Physiology Knee Physiology Extension Flexion Range of Motion Evaluation Human Male Female Adolescence Young Adult Athletes South Korea Academic Medical Centers Nonexperimental Studies Comparative Studies Descriptive Statistics Data Analysis Software Unpaired T-Tests Chi Square Test ROC Curve Logistic Regression Random Forest Support Vector Machine Decision Trees Clinical Assessment Tools Balance, Postural Evaluation Goniometry Athletic Performance Classification Motion Analysis Systems Lower Extremity Physiology Adolescent: 13-18 years Male Female ab: To develop and validate machine learning (ML) models for classifying Taekwondo Poomsae side kick (SK) performance using kinematic parameters and physical function characteristics. Forty collegiate Taekwondo Poomsae athletes performed SKs with both legs. Two models were developed: a kinematic model incorporating SK and pelvic tilt angles at face and maximal heights, and a physical function model including range of motion measurements and Y-balance test scores. Performance quality was assessed by an expert evaluator using standardised criteria. Five ML algorithms were tested, and their performance was evaluated using area under the curve (AUC) analysis. Random forest classifiers demonstrated excellent performance in both models (kinematic model: AUC = 0.930, accuracy = 89.3%; physical function model: AUC = 0.930, accuracy = 89.3%). In the kinematic model, SK angle at maximal height emerged as the strongest predictor. For the physical function model, Y-balance test composite score showed the largest impact. These findings represent a substantial improvement over conventional subjective assessment methods by providing quantifiable, objective classification with high accuracy. ML algorithms can effectively classify Taekwondo SK performance using both kinematic and physical function parameters. SK angle at maximal height and dynamic balance emerged as the most important predictors in their respective models, providing quantitative criteria for performance assessment. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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