Cargando…

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

Currently, gathering statistics and information for ice hockey training purposes mostly happens by hand, whereas the automated systems that do exist are expensive and difficult to set up. To remedy this, in this paper, we propose and analyse a wearable system that combines player localisation and ac...

Descripción completa

Detalles Bibliográficos
Autores principales: Vleugels, Robbe, Van Herbruggen, Ben, Fontaine, Jaron, De Poorter, Eli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309498/
https://www.ncbi.nlm.nih.gov/pubmed/34300390
http://dx.doi.org/10.3390/s21144650
_version_ 1783728535664328704
author Vleugels, Robbe
Van Herbruggen, Ben
Fontaine, Jaron
De Poorter, Eli
author_facet Vleugels, Robbe
Van Herbruggen, Ben
Fontaine, Jaron
De Poorter, Eli
author_sort Vleugels, Robbe
collection PubMed
description Currently, gathering statistics and information for ice hockey training purposes mostly happens by hand, whereas the automated systems that do exist are expensive and difficult to set up. To remedy this, in this paper, we propose and analyse a wearable system that combines player localisation and activity classification to automatically gather information. A stick-worn inertial measurement unit was used to capture acceleration and rotation data from six ice hockey activities. A convolutional neural network was able to distinguish the six activities from an unseen player with a 76% accuracy at a sample frequency of 100 Hz. Using unseen data from players used to train the model, a 99% accuracy was reached. With a peak detection algorithm, activities could be automatically detected and extracted from a complete measurement for classification. Additionally, the feasibility of a time difference of arrival based ultra-wideband system operating at a 25 Hz update rate was determined. We concluded that the system, when the data were filtered and smoothed, provided acceptable accuracy for use in ice hockey. Combining both, it was possible to gather useful information about a wide range of interesting performance measures. This shows that our proposed system is a suitable solution for the analysis of ice hockey.
format Online
Article
Text
id pubmed-8309498
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-83094982021-07-25 Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics Vleugels, Robbe Van Herbruggen, Ben Fontaine, Jaron De Poorter, Eli Sensors (Basel) Article Currently, gathering statistics and information for ice hockey training purposes mostly happens by hand, whereas the automated systems that do exist are expensive and difficult to set up. To remedy this, in this paper, we propose and analyse a wearable system that combines player localisation and activity classification to automatically gather information. A stick-worn inertial measurement unit was used to capture acceleration and rotation data from six ice hockey activities. A convolutional neural network was able to distinguish the six activities from an unseen player with a 76% accuracy at a sample frequency of 100 Hz. Using unseen data from players used to train the model, a 99% accuracy was reached. With a peak detection algorithm, activities could be automatically detected and extracted from a complete measurement for classification. Additionally, the feasibility of a time difference of arrival based ultra-wideband system operating at a 25 Hz update rate was determined. We concluded that the system, when the data were filtered and smoothed, provided acceptable accuracy for use in ice hockey. Combining both, it was possible to gather useful information about a wide range of interesting performance measures. This shows that our proposed system is a suitable solution for the analysis of ice hockey. MDPI 2021-07-07 /pmc/articles/PMC8309498/ /pubmed/34300390 http://dx.doi.org/10.3390/s21144650 Text en © 2021 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
Vleugels, Robbe
Van Herbruggen, Ben
Fontaine, Jaron
De Poorter, Eli
Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title_full Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title_fullStr Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title_full_unstemmed Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title_short Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics
title_sort ultra-wideband indoor positioning and imu-based activity recognition for ice hockey analytics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309498/
https://www.ncbi.nlm.nih.gov/pubmed/34300390
http://dx.doi.org/10.3390/s21144650
work_keys_str_mv AT vleugelsrobbe ultrawidebandindoorpositioningandimubasedactivityrecognitionforicehockeyanalytics
AT vanherbruggenben ultrawidebandindoorpositioningandimubasedactivityrecognitionforicehockeyanalytics
AT fontainejaron ultrawidebandindoorpositioningandimubasedactivityrecognitionforicehockeyanalytics
AT depoortereli ultrawidebandindoorpositioningandimubasedactivityrecognitionforicehockeyanalytics