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Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning
journal contribution
posted on 2020-03-09, 15:06 authored by Hannah JowittHannah Jowitt, Jérôme Durussel, Raphael Brandon, Mark KingMark KingCricket fast bowlers are at a high risk of injury occurrence, which has previously been shown to be correlated to bowling workloads. This study aimed to develop and test an algorithm that can automatically, reliably and accurately detect bowling deliveries. Inertial sensor data from a Catapult OptimEye S5 wearable device was collected from both national and international level fast bowlers (n = 35) in both training and matches, at various intensities. A machine-learning based approach was used to develop the algorithm. Outputs were compared with over 20,000 manually recorded events. A high Matthews correlation coefficient (r = 0.945) showed very good agreement between the automatically detected bowling deliveries and manually recorded ones. The algorithm was found to be both sensitive and specific in training (96.3%, 98.3%) and matches (99.6%, 96.9%), respectively. Rare falsely classified events were typically warm-up deliveries or throws preceded by a run. Inertial sensors data processed by a machine-learning based algorithm provide a valid tool to automatically detect bowling events, whilst also providing the opportunity to look at performance metrics associated with fast bowling. This offers the possibility to better monitor bowling workloads across a range of intensities to mitigate injury risk potential and maximise performance.
History
School
- Sport, Exercise and Health Sciences
Published in
Journal of Sports SciencesVolume
38Issue
7Pages
767 - 772Publisher
Taylor and FrancisVersion
- AM (Accepted Manuscript)
Rights holder
© Informa UK Limited, trading as Taylor and Francis GroupPublisher statement
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Sports Sciences on 26 Feb 2020, available online: https://doi.org/10.1080/02640414.2020.1734308Acceptance date
2019-10-29Publication date
2020-02-26Copyright date
2020ISSN
0264-0414eISSN
1466-447XPublisher version
Language
- en
Depositor
Prof Mark King Deposit date: 8 March 2020Usage metrics
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