Machine learning-based classification of ice hockey skating tasks using kinematic data.
This study evaluates the ability of body segment kinematic data to identify skating tasks in ice hockey using machine learning models and compares the performance of models trained on different body segments. We employed XGBoost, Support Vector Machine and Random Forest models to classify four prima...
| Publicado en: | Sports Biomechanics Vol. 25; no. 5; pp. 659 - 673 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2026
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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=195794332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195794332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14763141 I7F jtl: Sports Biomechanics issn: 14763141 maglogo: N pubinfo: dt: May2026 vid: 25 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195794332 188536966 195794332 195794332 10.1080/14763141.2025.2569580 195794332 ppf: 659 ppct: 14 formats: tig: atl: Machine learning-based classification of ice hockey skating tasks using kinematic data. aug: au: Jlassi, Oussama Wilkie, Ethan W. C. Kelly, Matthew Renaud, Philippe J. Pearsall, David J. Robbins, Shawn M. Dixon, Philippe C. affil: Department of Kinesiology and Physical Education, Mcgill University, Montreal, Canada sug: subj: Ice Skating Hockey Kinematics Machine Learning Task Performance and Analysis Athletic Performance Evaluation Prediction Algorithms Classification Algorithms Acceleration Funding Source Human Male Female Boosting Machine Learning Algorithms Support Vector Machine Random Forest Secondary Analysis Descriptive Statistics Pelvis Physiology Torso Physiology Foot Physiology Male Female ab: This study evaluates the ability of body segment kinematic data to identify skating tasks in ice hockey using machine learning models and compares the performance of models trained on different body segments. We employed XGBoost, Support Vector Machine and Random Forest models to classify four primary ice-hockey skating tasks: forward skating start and strides, skating stop & go, and skating into a wrist shot. Trunk, pelvis, thigh, shank, and foot segment centre of mass linear accelerations were derived from retro-reflective markers and used as inputs for feature engineering. The models were trained and evaluated using a 10-fold cross-validation stratified by participant. Overall, the machine learning models demonstrated strong performance, with mean accuracy scores ranging from 86.5% to 98.9%. The pelvis yielded the best overall performance, followed by the trunk and foot, whereas the thigh segment generally exhibited lower accuracies across models. These results indicate that prediction performance depends on the body segment kinematic data used as input. This study highlights the potential of body segment kinematic data for automated identification of ice hockey skating tasks, providing insights into sports analytics and player performance assessment. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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