title: Statistical method for prediction of gait kinematics with Gaussian process regression.
abstract: 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.
authors.affiliation: Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA. Electronic address: yunyoungmok@utexas.edu.
authors.forename: Youngmok
authors.initials: Y
authors.lastname: Yun
authors.affiliation: The Center of Bionics, Korea Institute of Science and Technology, Seoul, South Korea.
authors.forename: Hyun-Chul
authors.initials: HC
authors.lastname: Kim
authors.affiliation: The Center of Bionics, Korea Institute of Science and Technology, Seoul, South Korea.
authors.forename: Sung Yul
authors.initials: SY
authors.lastname: Shin
authors.affiliation: The Center of Bionics, Korea Institute of Science and Technology, Seoul, South Korea.
authors.forename: Junwon
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA.
authors.forename: Ashish D
authors.initials: AD
authors.lastname: Deshpande
authors.affiliation: The Center of Bionics, Korea Institute of Science and Technology, Seoul, South Korea. Electronic address: ckim@kist.re.kr.
authors.forename: Changhwan
authors.initials: C
authors.lastname: Kim
mesh_terms: D000328:Adult; D000465:Algorithms; D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D007596:Joints / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; D015233:Models, Statistical; D016011:Normal Distribution; D012044:Regression Analysis; D013269:Stochastic Processes
keywords: Gait pattern; Gaussian process regression; Statistics; Stochastic analysis