Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Amerineni, Rajesh, Gupta, Lalit, Steadman, Nathan, Annauth, Keshwyn, Burr, Charles, Wilson, Samuel, Barnaghi, Payam, Vaidyanathan, Ravi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1784622307814146048
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
work_keys_str_mv AT amerinenirajesh fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT guptalalit fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT steadmannathan fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT annauthkeshwyn fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT burrcharles fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT wilsonsamuel fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT barnaghipayam fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance
AT vaidyanathanravi fusionmodelsforgeneralizedclassificationofmultiaxialhumanmovementvalidationinsportperformance