Fusion models for generalized classification of multi-axial human movement: validation in sport performance
File(s)
Published version
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Author(s)
Type
Journal Article
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.
Date Issued
2021-12-16
Date Acceptance
2021-12-06
Citation
Sensors, 2021, 21 (24)
ISSN
1424-8220
Publisher
MDPI AG
Journal / Book Title
Sensors
Volume
21
Issue
24
Copyright Statement
© 2021 by the authors.
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/)
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/)
License URL
Sponsor
Engineering & Physical Science Research Council (E
Medical Research Council
Grant Number
EP/R511547/1
UKDRI-7003
Subjects
0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
Analytical Chemistry
Publication Status
Published
Article Number
ARTN 8409