Predicting coordination variability of selected lower extremity couplings during a cutting movement: an investigation of deep neural networks with the LSTM structure

Enze Shao, Qichang Mei, Jingyi Ye, Ukadike C. Ugbolue, Chaoyi Chen, Yaodong Gu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

There are still few portable methods for monitoring lower limb joint coordination during the cutting movements (CM). This study aims to obtain the relevant motion biomechanical parameters of the lower limb joints at 90°, 135°, and 180° CM by collecting IMU data of the human lower limbs, and utilizing the Long Short-Term Memory (LSTM) deep neural-network framework to predict the coordination variability of selected lower extremity couplings at the three CM directions. There was a significant (p < 0.001) difference between the three couplings during the swing, especially at 90° vs the other directions. At 135° and 180°, the coordination variability of couplings was significantly greater than at 90° (p < 0.001). It is important to note that the coordination variability of Hip rotation /Knee flexion-extension was significantly higher at 90° than at 180° (p < 0.001). By the LSTM, the CM coordination variability for 90° (CMC = 0.99063, RMSE = 0.02358), 135° (CMC = 0.99018, RMSE = 0.02465) and 180° (CMC = 0.99485, RMSE = 0.01771) were accurately predicted. The predictive model could be used as a reliable tool for predicting the coordination variability of different CM directions in patients or athletes and real-world open scenarios using inertial sensors.
Original languageEnglish
Article number411
Number of pages16
JournalBioengineering
Volume9
Issue number9
DOIs
Publication statusPublished - 23 Aug 2022

Keywords

  • cutting movement
  • vector coding technique
  • inertial sensor
  • deep neural network

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