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Automatic classification of running surfaces using an ankle-worn inertial sensor

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Abstract

Running surfaces influence energy consumption and gait parameters including swing time and stance time. This paper compares running gait cycle time, swing time and stance time, recorded on an athletics track, soft sand, and hard sand. The training and evaluation of supervised machine learning models for running surface prediction were developed using an ankle-worn inertial sensor. Models were trained using statistical features extracted from six participants using gyroscope-based stride cycles. Six different model types were trained and the performance of each model was evaluated using precision, recall, F1-score, Matthews correlation coefficient, area under the precision–recall curve and accuracy. There was a significant statistical difference in swing time and stance time across the surfaces for all participants (p < 0.05). Athlete-independent models demonstrated acceptable ability to distinguish soft sand from the two harder surfaces (≥ 0.75 mean precision, ≥ 0.90 mean recall, ≥ 0.83 mean F1-score, ≥ 0.98 mean area under the precision–recall curve across all models), but they were poor at differentiating between athletics track and hard sand. The athlete-dependent models demonstrated strong ability to classify all the surfaces (weighted average precision, recall, F1-score, Matthews correlation coefficient, area under the precision–recall curve, and overall accuracy ≥ 96%). Support vector machine models were the best in both athlete-independent and athlete-dependent methodologies. Features extracted from an ankle-worn inertial sensor can be used to classify running surface with high performance, when models are trained using features pertinent to each athlete.

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Acknowledgements

This project was aided by an affiliation of the lead researcher with the Sport Performance Innovation and Knowledge Excellence (SPIKE) unit of the Queensland Academy of Sport. We thank the voluntary participants who agreed to run for this research project (Ethics approval GU 2017/587). M.T.O.W. received a PhD scholarship funding from Griffith University.

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Authors and Affiliations

  1. Griffith University Sports Technology (GUST), School of Engineering and Built Environment, Griffith University, 170 Kessles Rd. Nathan Campus, Brisbane, QLD, 4111, Australia

    Matthew T. O. Worsey, Hugo G. Espinosa, Jonathan B. Shepherd & David V. Thiel

  2. Queensland Academy of Sport, Queensland Sport and Athletics Centre, 468 Kessels Rd, Nathan, QLD, 4111, Australia

    Matthew T. O. Worsey

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  1. Matthew T. O. Worsey
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  2. Hugo G. Espinosa
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Correspondence to Hugo G. Espinosa.

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Hugo Espinosa is an Associate Editor for Sports Engineering, David Thiel serves on the Editorial Board of Sports Engineering and Jonathan Shepherd is the President of the International Sports Engineering Association (ISEA), and they were not involved in the blind peer review process of this work.

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Ethics approval GU 2017/587.

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This article is a part of Topical Collection in Sports Engineering on Wearable Sensor Technology in Sports Monitoring, Edited by Dr. Hugo G Espinosa, Dr. Aimee Mears, Prof. Andy Stamm, Prof. Yuji Ohgi and Ms. Christine Coniglio.

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Worsey, M.T.O., Espinosa, H.G., Shepherd, J.B. et al. Automatic classification of running surfaces using an ankle-worn inertial sensor. Sports Eng 24, 22 (2021). https://doi.org/10.1007/s12283-021-00359-w

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