Classification of runners’ performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Shiwei Mo
  • Vincent C.K. Cheung
  • Ben M.F. Cheung
  • Shuotong Wang
  • Peter P.K. Chan
  • Akash Malhotra
  • Roy T.H. Cheung

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number110072
Journal / PublicationJournal of Biomechanics
Volume112
Online published8 Oct 2020
Publication statusPublished - 9 Nov 2020

Abstract

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner's performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners’ performance levels and biomechanical parameters.

Research Area(s)

  • Inertial measurement unit, Machine learning, Running biomechanics, Wearable sensor

Citation Format(s)

Classification of runners’ performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units. / Liu, Qi; Mo, Shiwei; Cheung, Vincent C.K.; Cheung, Ben M.F.; Wang, Shuotong; Chan, Peter P.K.; Malhotra, Akash; Cheung, Roy T.H.; Chan, Rosa H.M.

In: Journal of Biomechanics, Vol. 112, 110072, 09.11.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review