Improvement of a Forward Dynamic MPC Based Human Gait Model

2016 ◽  
Author(s):  
Shaoli Wu ◽  
Philip A. Voglewede
Keyword(s):  
Predictive Control ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Human Gait ◽  
Mechanical Parameters ◽  
Anthropometric Parameters ◽  
Robust Controller ◽  
Central Tenet ◽  
Human Joints ◽  
Improved Model

This paper develops an improvement to an existing forward dynamic human gait model. A human gait model was developed previously to assist virtual testing prostheses and orthoses. The model consists of a plant model and a controller model. The central tenet to the model is the model predictive control (MPC) algorithm, which is a highly robust controller. In the previous model, however, there are several drawbacks. First, the anthropometric and mechanical parameters in the parts of the model are specific to one person. Second, the simulation result of ground reaction force (GRF) is not realistic. In this paper, the anthropometric parameters are calculated based on commonly used models that approximate an average person’s size. As for the mechanical parameters, the spring and damper coefficients in the human joints and ground reaction force (GRF) system are estimated by using the parameter estimation module in MATLAB based on the experimental subject data. The paper concludes with a simulation results between the new improved model and the previous developed model.

scholarly journals Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fabian Horst ◽  
Djordje Slijepcevic ◽  
Marvin Simak ◽  
Wolfgang I. Schöllhorn
Keyword(s):  
Ground Reaction Force ◽  
Center Of Pressure ◽  
Reaction Force ◽  
Three Dimensional ◽  
Human Gait ◽  
Healthy Individuals ◽  
Overground Walking ◽  
Gait Patterns ◽  
Specific Factors ◽  
Gait Database

AbstractThe Gutenberg Gait Database comprises data of 350 healthy individuals recorded in our laboratory over the past seven years. The database contains ground reaction force (GRF) and center of pressure (COP) data of two consecutive steps measured - by two force plates embedded in the ground - during level overground walking at self-selected walking speed. The database includes participants of varying ages, from 11 to 64 years. For each participant, up to eight gait analysis sessions were recorded, with each session comprising at least eight gait trials. The database provides unprocessed (raw) and processed (ready-to-use) data, including three-dimensional GRF and two-dimensional COP signals during the stance phase. These data records offer new possibilities for future studies on human gait, e.g., the application as a reference set for the analysis of pathological gait patterns, or for automatic classification using machine learning. In the future, the database will be expanded continuously to obtain an even larger and well-balanced database with respect to age, sex, and other gait-specific factors.

Asymmetries in ground reaction force patterns in normal human gait

1989 ◽  
Vol 21 (1) ◽  
pp. 110-114 ◽  
Author(s):  
WALTER HERZOG ◽  
BENNO M. NIGG ◽  
LYNDA J. READ ◽  
EWA OLSSON
Keyword(s):  
Ground Reaction Force ◽  
Reaction Force ◽  
Human Gait ◽  
Normal Human

Reconstruction of Ground Reaction Force Data Using Lyapunov Floquet Theory and Invariant Manifold Theory

2020 ◽  
Author(s):  
Sandesh G. Bhat ◽  
Thomas G. Sugar ◽  
Sangram Redkar
Keyword(s):  
System Identification ◽  
Invariant Manifold ◽  
Ground Reaction Force ◽  
Order Approximation ◽  
Reaction Force ◽  
Human Gait ◽  
Order System ◽  
Conformal Map ◽  
Reduced Order ◽  
Manifold Theory

Abstract Ground Reaction Force (GRF) is an essential gait parameter. GRF analysis provides important information regarding various aspects of gait. GRF has been traditionally measured using bulky force plates within lab environments. There exist portable force sensing units, but their accuracy is wanting. Estimation of GRF has applications in remote wearable systems for rehabilitation, to measure performance in athletes, etc. This article explores a novel method for GRF estimation using the Lyapunov-Floquet (LF) and invariant manifold theory. We assume human gait to be a periodic motion without external forcing. Using time delayed embedding, a reduced order system can be reconstructed from the vertical GRF data. LF theory can be applied to perform system identification via Floquet Transition Matrix and the Lyapunov Exponents. A Conformal Map was generated using the Lyapunov Floquet Transformation that maps the original time periodic system on a linear Single Degree of Freedom (SDoF) oscillator. The response of the oscillator system can be calculated numerically and then remapped back to the original domain to get GRF time evolution. As an example, the GRF data from an optical motion capture system for two subjects was used to construct the reduced order model and system identification. A comparison between the original system and its reduced order approximation showed good correspondence.

Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification

2020 ◽  
Author(s):  
Rami Alkhatib ◽  
Mohamad O. Diab ◽  
Christophe Corbier ◽  
Mohamed El Badaoui
Keyword(s):  
Neural Network ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
The Elderly ◽  
Ensemble Empirical Mode Decomposition ◽  
Human Gait ◽  
Walking Ability ◽  
Intrinsic Mode Functions ◽  
Vertical Ground Reaction Force ◽  
Mode Decomposition

Abstract Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.

Sign in / Sign up

Export Citation Format

Share Document