title: Classification of team sport activities using a single wearable tracking device.
abstract: 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.
authors.affiliation: Centre for Exercise & Sports Science, School of Exercise & Nutrition Sciences, Deakin University, Melbourne, Victoria, Australia. Electronic address: dwunder@deakin.edu.au.
authors.forename: Daniel W T
authors.initials: DWT
authors.lastname: Wundersitz
authors.affiliation: Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia.
authors.forename: Casey
authors.initials: C
authors.lastname: Josman
authors.affiliation: Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia.
authors.forename: Ritu
authors.initials: R
authors.lastname: Gupta
authors.affiliation: School of Physiotherapy and Exercise Science, Curtin University, Perth, Western Australia, Australia.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Netto
authors.affiliation: Centre for Exercise & Sports Science, School of Exercise & Nutrition Sciences, Deakin University, Melbourne, Victoria, Australia.
authors.forename: Paul B
authors.initials: PB
authors.lastname: Gastin
authors.affiliation: Centre for Exercise & Sports Science, School of Exercise & Nutrition Sciences, Deakin University, Melbourne, Victoria, Australia; Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Victoria, Australia.
authors.forename: Sam
authors.initials: S
authors.lastname: Robertson
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D000328:Adult; D000465:Algorithms; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009068:Movement / Q000502:physiology*; D013177:Sports / Q000145:classification* / Q000502:physiology*; D060388:Support Vector Machine; D055815:Young Adult
keywords: Accelerometer; Gyroscope; Logistic Regression Tree; Random Forest; Support Vector Machine