Statistical Method for Prediction of Human Walking Pattern with Gaussian Process Regression
We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject’s body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.
Journal of Biomechanics, 47 (1), pp. 186 - 192, 2014, ISSN: 0021-9290.
Synthesis of Gait Kinematics Using a Database Conference
Dynamic Walking, 2013.
Statistical Functional Mapping From Body Parameters to Gait Kinematics Conference
American Society of Biomechanics, 2013.