Cross-leg prediction of running kinematics across various running conditions and drawing from a minimal data set using a single wearable sensor

Hung Kay Daniel CHOW, Zaheen Ahmed IQBAL, Luc TREMBLAY, Chor-Yin LAM, Ruibin ZHAO

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set. Copyright © 2022 by the authors.
Original languageEnglish
Article number1092
JournalSymmetry
Volume14
Issue number6
DOIs
Publication statusPublished - May 2022

Citation

Chow, D. H.-K., Iqbal, Z. A., Tremblay, L., Lam, C.-Y., & Zhao, R.-B. (2022). Cross-leg prediction of running kinematics across various running conditions and drawing from a minimal data set using a single wearable sensor. Symmetry, 14(6). Retrieved from https://doi.org/10.3390/sym14061092

Keywords

  • Deep learning
  • Convolutional neural network
  • Running kinematics
  • Wearable sensor
  • Gyroscope
  • PG student publication

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