ABSTRACT
Background: Human gait varies based on personal characteristics, the existence of walking problems, or variability of gait parameters. Identifying the sources of variations is significant in detecting walking problems, designing orthotic/prosthetic products, etc. Research questions: What are the types of variations in joint angles and ground reaction forces? How do age, sex, height, weight, and walking speed affect the distribution of the modes? Methods: In this study, temporal variations in the joint angles and ground reaction forces were obtained using singular value decomposition. Then, the relationships among age, sex, height, weight, walking speed, and the coefficients obtained by singular value decomposition were investigated using Pearson's correlation coefficient matrix. Results: The first mode of joint angles and ground reaction forces represent the overall characteristics; the first six modes of joint angles and the first two modes of ground reaction forces express 99.9% of the gait parameter space. We concluded that the walking speed dramatically affects joint kinematics and ground reaction forces. In addition, the effects of age, gender, height, and weight on the joint kinematics and ground reaction forces were also found, but with less contribution. Significance: The temporal behavior of the joint angles and ground reaction forces was expressed using a few coefficients due to singular value decomposition. The effects of age, sex, weight, height, and walking speed on the modes were found. The proposed method can be applied to understand the progression of an abnormality, and design orthotic/prosthetic products etc. in future studies.
ABSTRACT
Walking is an everyday activity and contains variations from person to person, from one step to another step. The variation may occur due to the uniqueness of each gait cycle, personal parameters, such as age, walking speed, etc., and the existence of a gait abnormality. Understanding the normal variation depending on personal parameters helps medical experts to identify deviations from normal gait and engineers to design compatible orthotic and prosthetic products. In the present study, we aimed to obtain normal gait variations based on age, sex, height, weight, and walking speed. For this purpose, a large dataset of walking trials was used to model normal walking. An artificial neural network-based gait characterization model is proposed to show the relation between personal parameters and gait parameters. The neural network model simulates normal walking by considering the effect of personal parameters. The predicted behavior of gait parameters by artificial neural network model has a similarity with existing literature. The differences between experimental data and the neural network model were calculated. To determine how much deviation between predictions and experiments can be considered excessive, the distributions of differences for each gait parameter were obtained. The phases of walking in which excessive differences were intensified were determined. It was revealed that the artificial neural network-based gait characterization model exhibits the behavior of the normal gait parameters depending on the personal parameters.
