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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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 |
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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 |
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