Classification of team sport activities using a single wearable tracking device

Wundersitz, Daniel W.T., Josman, Casey, Gupta, Ritu, Netto, Kevin J., Gastin, Paul B. and Robertson, Sam 2015, Classification of team sport activities using a single wearable tracking device, Journal of biomechanics, vol. 48, no. 15, pp. 3975-3981, doi: 10.1016/j.jbiomech.2015.09.015.

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Title Classification of team sport activities using a single wearable tracking device
Author(s) Wundersitz, Daniel W.T.
Josman, Casey
Gupta, Ritu
Netto, Kevin J.
Gastin, Paul B.ORCID iD for Gastin, Paul B.
Robertson, Sam
Journal name Journal of biomechanics
Volume number 48
Issue number 15
Start page 3975
End page 3981
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-11-26
ISSN 1873-2380
Keyword(s) Accelerometer
Logistic Regression Tree
Random Forest
Support Vector Machine
Summary Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.
Language eng
DOI 10.1016/j.jbiomech.2015.09.015
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
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Document type: Journal Article
Collections: Faculty of Health
School of Exercise and Nutrition Sciences
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