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Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance
We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible...
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/PMC8706912/ https://www.ncbi.nlm.nih.gov/pubmed/34960500 http://dx.doi.org/10.3390/s21248409 |
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author | Amerineni, Rajesh Gupta, Lalit Steadman, Nathan Annauth, Keshwyn Burr, Charles Wilson, Samuel Barnaghi, Payam Vaidyanathan, Ravi |
author_facet | Amerineni, Rajesh Gupta, Lalit Steadman, Nathan Annauth, Keshwyn Burr, Charles Wilson, Samuel Barnaghi, Payam Vaidyanathan, Ravi |
author_sort | Amerineni, Rajesh |
collection | PubMed |
description | We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation. |
format | Online Article Text |
id | pubmed-8706912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87069122021-12-25 Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance Amerineni, Rajesh Gupta, Lalit Steadman, Nathan Annauth, Keshwyn Burr, Charles Wilson, Samuel Barnaghi, Payam Vaidyanathan, Ravi Sensors (Basel) Article We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation. MDPI 2021-12-16 /pmc/articles/PMC8706912/ /pubmed/34960500 http://dx.doi.org/10.3390/s21248409 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 Amerineni, Rajesh Gupta, Lalit Steadman, Nathan Annauth, Keshwyn Burr, Charles Wilson, Samuel Barnaghi, Payam Vaidyanathan, Ravi Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title | Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title_full | Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title_fullStr | Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title_full_unstemmed | Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title_short | Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance |
title_sort | fusion models for generalized classification of multi-axial human movement: validation in sport performance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706912/ https://www.ncbi.nlm.nih.gov/pubmed/34960500 http://dx.doi.org/10.3390/s21248409 |
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