1. Human-exoskeleton combined model
Predictive Simulation Framework for Combined Device and Human Mechanics
Mohammadhossein Saadatzi and Ozkan Celik
Department of Mechanical Engineering, Colorado School of Mines, Golden, CO
Web:
The poster is 36” wide by 48” tall
Objective
Development of a framework for predictive simulation of bipedal walking as a tool for study and design of exoskeletons/prostheses.
Methods
The computational framework makes use OpenSim API in C++.
Our framework generates bipedal walking through optimization of muscle actuation profiles,
eliminating the need for manually-tuned reflex control laws[3,4].
improves the exploration ability of the optimization.
facilitates consideration of different human musculoskeletal models.
Introduction
Wearable robots are recently being explored to make walking more efficient.
Human adaptation is a primary challenge in design of wearable robots and their controllers (the difficulty in device performance prediction).
Design and control of these devices thus far have been primarily carried out following exhaustive experimental (design and test) procedures .
Combined predictive simulations carry the promise to address this challenge and significantly accelerate design, testing and implementation of wearable robots.
We use Open Science Grid [5], an open source high-throughput computing (HTC) resource, to run multiple simultaneous optimizations so as to identify the solutions that are as close as possible to the global minimum.
Human-exoskeleton combined model
Actuation profile
Two controller states have been considered, corresponding to support and swing phases.
Forward simulation
Actuation profile of each muscle is defined as a piecewise linear function with 10 parameters (6 parameters for stance phase, and 4 parameters for swing phase).
Exoskeleton structure and control parameters
Our current OpenSim musculoskeletal model contains 9 sagittal DOF.
Human-exoskeleton kinematic and dynamic data
The Vanderbilt exoskeleton shown in this block is a representative human augmenting device [3].
Design Parameters (currently 90 ones)
Optimizer
Objective function
Covariance Matrix Adaptation (CMA) method available with OpenSim API
Actuation profiles are determined by minimizing proper cost functions (like, target velocity error, metabolic expenditure)
Results and Discussion
Conclusions
We presented initial results from the framework we have developed for the predictive simulation of bipedal walking.
The proposed framework can be used for design of various types of exoskeletons, including both passive and active, with feedback controllers or optimal actuation profiles.
We have successfully used our framework for the planar simulation of bipedal walking (Figs. 2 and 3).
Joint angles during 88.6 percent of the gait cycle (hip 100%, knee 76.95%, ankle 88.92%) are within 1 standard deviation of experimental data.
The ground reaction forces (GRF) of simulated walking reproduce the main features of experimental GRF.
A credible simulation of bipedal walking must closely follow experimental human gait data. We compare our results with experimental data presented by Dorn et al. in [4].
Extending our work to simulation of bipedal walking in three- dimensional space and running gait constitutes our future direction.
Video:
References
[1] Delp SL, et al. IEEE Trans Biomed Eng 54 (11), , 2007.
[2] Hansen N, et al. Evol Comput, 11(1) 1–18, 2003.
[3] Geyer H, Herr H, IEEE Trans Neural Syst Rehabil Eng 18(3), , 2010.
[4] Dorn TW, et al. PLOS ONE 10 (4), 1-16, 2015.
[5] Pordes, R et al. J Phys Conf Ser 78(1), , 2007.
Fig. 2 Joint kinematics
Fig. 3 Ground reaction forces.