Cargando…

Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting

The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed in stiffness, bla...

Descripción completa

Detalles Bibliográficos
Autores principales: Léger, Taylor, Renaud, Philippe J., Robbins, Shawn M., Pearsall, David J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105185/
https://www.ncbi.nlm.nih.gov/pubmed/35591104
http://dx.doi.org/10.3390/s22093419
_version_ 1784707978575740928
author Léger, Taylor
Renaud, Philippe J.
Robbins, Shawn M.
Pearsall, David J.
author_facet Léger, Taylor
Renaud, Philippe J.
Robbins, Shawn M.
Pearsall, David J.
author_sort Léger, Taylor
collection PubMed
description The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed in stiffness, blade pattern, or kick point). Custom IMUs were embedded in a pair of hockey gloves to capture resultant linear acceleration and angular velocity of the hands during shooting while an 18-camera optical motion capture system and retroreflective markers were used to identify key shot events and measure puck speed, accuracy, and contact time with the stick blade. MATLAB R2020a’s Machine Learning Toolbox was used to build and evaluate the performance of machine learning algorithms using principal components of the resultant hand kinematic signals using principal components accounting for 95% of the variability and a five-fold cross validation. Fine k-nearest neighbors algorithms were found to be highly accurate, correctly classifying players by optimal stick flex, blade pattern, and kick point with 90–98% accuracy for slap shots and 93–97% accuracy for wrist shots in fractions of a second. Based on these findings, it appears promising that wearable sensors and machine learning algorithms can be used for reliable, rapid, and portable hockey stick fitting.
format Online
Article
Text
id pubmed-9105185
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91051852022-05-14 Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting Léger, Taylor Renaud, Philippe J. Robbins, Shawn M. Pearsall, David J. Sensors (Basel) Article The aims of this study were to evaluate the feasibility of using IMU sensors and machine learning algorithms for the instantaneous fitting of ice hockey sticks. Ten experienced hockey players performed 80 shots using four sticks of differing constructions (i.e., each stick differed in stiffness, blade pattern, or kick point). Custom IMUs were embedded in a pair of hockey gloves to capture resultant linear acceleration and angular velocity of the hands during shooting while an 18-camera optical motion capture system and retroreflective markers were used to identify key shot events and measure puck speed, accuracy, and contact time with the stick blade. MATLAB R2020a’s Machine Learning Toolbox was used to build and evaluate the performance of machine learning algorithms using principal components of the resultant hand kinematic signals using principal components accounting for 95% of the variability and a five-fold cross validation. Fine k-nearest neighbors algorithms were found to be highly accurate, correctly classifying players by optimal stick flex, blade pattern, and kick point with 90–98% accuracy for slap shots and 93–97% accuracy for wrist shots in fractions of a second. Based on these findings, it appears promising that wearable sensors and machine learning algorithms can be used for reliable, rapid, and portable hockey stick fitting. MDPI 2022-04-29 /pmc/articles/PMC9105185/ /pubmed/35591104 http://dx.doi.org/10.3390/s22093419 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Léger, Taylor
Renaud, Philippe J.
Robbins, Shawn M.
Pearsall, David J.
Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title_full Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title_fullStr Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title_full_unstemmed Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title_short Pilot Study of Embedded IMU Sensors and Machine Learning Algorithms for Automated Ice Hockey Stick Fitting
title_sort pilot study of embedded imu sensors and machine learning algorithms for automated ice hockey stick fitting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105185/
https://www.ncbi.nlm.nih.gov/pubmed/35591104
http://dx.doi.org/10.3390/s22093419
work_keys_str_mv AT legertaylor pilotstudyofembeddedimusensorsandmachinelearningalgorithmsforautomatedicehockeystickfitting
AT renaudphilippej pilotstudyofembeddedimusensorsandmachinelearningalgorithmsforautomatedicehockeystickfitting
AT robbinsshawnm pilotstudyofembeddedimusensorsandmachinelearningalgorithmsforautomatedicehockeystickfitting
AT pearsalldavidj pilotstudyofembeddedimusensorsandmachinelearningalgorithmsforautomatedicehockeystickfitting