Generating Individual Gait Kinetic Patterns Using Machine Learning

  • César BouçasEmail author
  • João P. Ferreira
  • A. Paulo Coimbra
  • Manuel M. Crisóstomo
  • Paulo A. S. Mendes
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)


In this study, data of 42 healthy individuals walking over a treadmill was used to train and test a neural network that produced individual kinetic patterns of gait cycle as output for a set of atomic features (gender, age, mass, height and gait speed) used as input. The proposed method implements a 3-layer feedforward architecture capable to produce the 3D gait patterns of ankle, knee and hip moment at once, with an average root mean squared error (RMSE) of 7% and average correlation coefficient (\(\rho \)) of 0.94 with respect to the ground truth patterns of the test set. The presented strategy may be used to support individual gait clinical analysis as an alternative to the use of the normal literature pattern that do not take into account the specific characteristics of the patients.


Human gait kinetics Time series generation Machine Learning 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • César Bouças
    • 1
    Email author
  • João P. Ferreira
    • 1
    • 2
  • A. Paulo Coimbra
    • 1
  • Manuel M. Crisóstomo
    • 1
  • Paulo A. S. Mendes
    • 1
  1. 1.Institute of Systems and Robotics, Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.Department of Electrical EngineeringSuperior Institute of Engineering of CoimbraCoimbraPortugal

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