Yun, Youngmok Kim, Hyun-Chul Shin, Sung Yul Lee, Junwon Deshpande, Ashish D. Kim, Changhwan 2024-01-20T10:33:56Z 2024-01-20T10:33:56Z 2021-09-04 2014-01-03 0021-9290 https://pubs.kist.re.kr/handle/201004/127233 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. (C) 2013 Elsevier Ltd. All rights reserved. English ELSEVIER SCI LTD HUMAN WALKING TEMPORAL PARAMETERS OPTIMIZATION BIOMECHANICS SIMULATIONS PATTERNS ADULTS RANGE Statistical method for prediction of gait kinematics with Gaussian process regression Article 10.1016/j.jbiomech.2013.09.032 1 JOURNAL OF BIOMECHANICS, v.47, no.1, pp.186 - 192 JOURNAL OF BIOMECHANICS 47 1 186 192 scie scopus 000331008200026 2-s2.0-84890856911 Biophysics Engineering, Biomedical Biophysics Engineering Article HUMAN WALKING TEMPORAL PARAMETERS OPTIMIZATION BIOMECHANICS SIMULATIONS PATTERNS ADULTS RANGE Gait pattern Statistics Gaussian process regression Stochastic analysis