Literature DB >> 24210476

Resonance-based oscillations could describe human gait mechanics under various loading conditions.

Myunghyun Lee1, Seyoung Kim2, Sukyung Park3.   

Abstract

The oscillatory behavior of the center of mass (CoM) and the corresponding ground reaction force (GRF) of human gait for various gait speeds can be accurately described in terms of resonance using a spring-mass bipedal model. Resonance is a mechanical phenomenon that reflects the maximum responsiveness and energetic efficiency of a system. To use resonance to describe human gait, we need to investigate whether resonant mechanics is a common property under multiple walking conditions. Body mass and leg stiffness are determinants of resonance; thus, in this study, we investigated the following questions: (1) whether the estimated leg stiffness increased with inertia, (2) whether a resonance-based CoM oscillation could be sustained during a change in the stiffness, and (3) whether these relationships were consistently observed for different walking speeds. Seven healthy young subjects participated in over-ground walking trials at three different gait speeds with and without a 25-kg backpack. We measured the GRFs and the joint kinematics using three force platforms and a motion capture system. The leg stiffness was incorporated using a stiffness parameter in a compliant bipedal model that best fitted the empirical GRF data. The results showed that the leg stiffness increased with the load such that the resonance-based oscillatory behavior of the CoM was maintained for a given gait speed. The results imply that the resonance-based oscillation of the CoM is a consistent gait property and that resonant mechanics may be useful for modeling human gait.
© 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Center of mass; Gait speed; Leg stiffness; Load; Resonance

Mesh:

Year:  2013        PMID: 24210476     DOI: 10.1016/j.jbiomech.2013.09.011

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  3 in total

1.  Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning.

Authors:  Hyerim Lim; Bumjoon Kim; Sukyung Park
Journal:  Sensors (Basel)       Date:  2019-12-24       Impact factor: 3.576

2.  Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum.

Authors:  Myunghyun Lee; Sukyung Park
Journal:  Sensors (Basel)       Date:  2020-11-04       Impact factor: 3.576

3.  Evaluation of Physical Interaction during Walker-Assisted Gait with the AGoRA Walker: Strategies Based on Virtual Mechanical Stiffness.

Authors:  Sergio D Sierra M; Marcela Múnera; Thomas Provot; Maxime Bourgain; Carlos A Cifuentes
Journal:  Sensors (Basel)       Date:  2021-05-07       Impact factor: 3.576

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.