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Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance

Authors :
Rajesh Amerineni
Lalit Gupta
Nathan Steadman
Keshwyn Annauth
Charles Burr
Samuel Wilson
Payam Barnaghi
Ravi Vaidyanathan
Source :
Sensors, Vol 21, Iss 24, p 8409 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
14248220
Volume :
21
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.6c01bd93ac0477b9fbdb90fcce12b47
Document Type :
article
Full Text :
https://doi.org/10.3390/s21248409