How gait nonlinearities in individuals without known pathology describe metabolic cost during walking using artificial neural network and multiple linear regression

HIGHLIGHTS

• What: The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization particularly for hip exoskeletons. The authors focused on the sagittal plane, as it captured key joint angles, moments, and velocities most relevant to forward progression and the metabolic cost while maintaining computational efficiency, given the sample size. The study designed and implemented ANNs to investigate the predictive power of nonlinear gait measures (LyER, DFA, ApEn, CD, SpEn, LyEW ) for metabolic cost. The resulting dataset, encompassing 120 trials, formed an extensive collection of biomechanical variables crucial to the aims of . . .
  

   

  

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