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Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data
Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focus...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417301/ https://www.ncbi.nlm.nih.gov/pubmed/32850706 http://dx.doi.org/10.3389/fbioe.2020.00814 |
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author | Ross, Gwyneth B. Dowling, Brittany Troje, Nikolaus F. Fischer, Steven L. Graham, Ryan B. |
author_facet | Ross, Gwyneth B. Dowling, Brittany Troje, Nikolaus F. Fischer, Steven L. Graham, Ryan B. |
author_sort | Ross, Gwyneth B. |
collection | PubMed |
description | Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focused on the development of an objective movement assessment tool to classify elite and novice athletes’ kinematic data using machine learning algorithms. Classifying elite and novice athletes can be beneficial to objectively detect differences in movement patterns between the athletes, which can then be used to provide higher quality feedback to athletes and their coaches. Currently, the method requires optical motion capture, which is expensive and time-consuming to use, creating a barrier for adoption within industry. Therefore, the purpose of this study was to assess whether machine learning could classify athletes as elite or novice using data that can be collected easily and inexpensively in the field using inertial measurement units (IMUs). A secondary purpose of this study was to refine the architecture of the tool to optimize classification rates. Motion capture data from 542 athletes performing seven dynamic screening movements were analyzed. A principal component analysis (PCA)-based pattern recognition technique and machine learning algorithms with the Euclidean norm of the segment linear accelerations and angular velocities as inputs were used to classify athletes based on skill level. Depending on the movement, using metrics achievable with IMUs and a linear discriminant analysis (LDA), 75.1–84.7% of athletes were accurately classified as elite or novice. We have provided evidence that suggests our objective, data-driven method can detect meaningful differences during a movement screening battery when using data that can be collected using IMUs, thus providing a large methodological advance as these can be collected in the field using sensors. This method offers an objective, inexpensive tool that can be easily implemented in the field to potentially enhance screening, assessment, and rehabilitation in sport and clinical settings. |
format | Online Article Text |
id | pubmed-7417301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74173012020-08-25 Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data Ross, Gwyneth B. Dowling, Brittany Troje, Nikolaus F. Fischer, Steven L. Graham, Ryan B. Front Bioeng Biotechnol Bioengineering and Biotechnology Movement screens are frequently used to identify differences in movement patterns such as pathological abnormalities or skill related differences in sport; however, abnormalities are often visually detected by a human assessor resulting in poor reliability. Therefore, our previous research has focused on the development of an objective movement assessment tool to classify elite and novice athletes’ kinematic data using machine learning algorithms. Classifying elite and novice athletes can be beneficial to objectively detect differences in movement patterns between the athletes, which can then be used to provide higher quality feedback to athletes and their coaches. Currently, the method requires optical motion capture, which is expensive and time-consuming to use, creating a barrier for adoption within industry. Therefore, the purpose of this study was to assess whether machine learning could classify athletes as elite or novice using data that can be collected easily and inexpensively in the field using inertial measurement units (IMUs). A secondary purpose of this study was to refine the architecture of the tool to optimize classification rates. Motion capture data from 542 athletes performing seven dynamic screening movements were analyzed. A principal component analysis (PCA)-based pattern recognition technique and machine learning algorithms with the Euclidean norm of the segment linear accelerations and angular velocities as inputs were used to classify athletes based on skill level. Depending on the movement, using metrics achievable with IMUs and a linear discriminant analysis (LDA), 75.1–84.7% of athletes were accurately classified as elite or novice. We have provided evidence that suggests our objective, data-driven method can detect meaningful differences during a movement screening battery when using data that can be collected using IMUs, thus providing a large methodological advance as these can be collected in the field using sensors. This method offers an objective, inexpensive tool that can be easily implemented in the field to potentially enhance screening, assessment, and rehabilitation in sport and clinical settings. Frontiers Media S.A. 2020-08-04 /pmc/articles/PMC7417301/ /pubmed/32850706 http://dx.doi.org/10.3389/fbioe.2020.00814 Text en Copyright © 2020 Ross, Dowling, Troje, Fischer and Graham. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Ross, Gwyneth B. Dowling, Brittany Troje, Nikolaus F. Fischer, Steven L. Graham, Ryan B. Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title | Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title_full | Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title_fullStr | Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title_full_unstemmed | Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title_short | Classifying Elite From Novice Athletes Using Simulated Wearable Sensor Data |
title_sort | classifying elite from novice athletes using simulated wearable sensor data |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417301/ https://www.ncbi.nlm.nih.gov/pubmed/32850706 http://dx.doi.org/10.3389/fbioe.2020.00814 |
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