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- W2317902007 abstract "The current research at the Human Motion & Control Laboratory in Cleveland is focused on the identification and application of biologically inspired controllers to powered prosthetics. My work is based around data driven approaches to identify feedback controllers capable of reproducing natural walking and running. We have been working with controllers structured to connect the sensors and actuators available on non-neurologically connected prosthetics. There are two approaches we are working with: (1) direct identification given measurements or estimates of the controllers’ inputs and outputs and (2) indirect identification given the measurements of the system’s kinematics and a plant model. We have so far focused on direct identification; however the indirect method has been become more appealing to us as biased results are not a concern as with direct identification. In addition, a major downside of indirect identification is that computational difficulty is much higher. This proposal details how we can use the expertise available at the National Center for Simulation Rehabilitation Research (NCSRR) to help make indirect identification methods for closed-loop gait control attainable. During the 2014 NCSRR visiting scholar program, I intend to extend the optimal control work presented in the paper “Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives” by Wang et. al [2] in such a way that feedback controller parameters are identified by minimizing the difference in the simulation’s kinematics and the measured kinematics from a large set of data. The data will be collected from a sample of subjects walking and running while under the influence of random lateral and longitudinal perturbations. I hypothesize the resulting identified controller will be more realistic than any other existing models, due to the fact that the controller will reproduce a variety of gait motions in which the subject is recovering, i.e. using feedback, from perturbations. Also, with careful attention to model complexity, simulation speed, and optimization methods I believe we can increase the speed of the computations such that simulations of 1 to 10 minutes are computationally tractable using modern cloud computing resources. Iteration times will likely be similar to or less than those presented in [2], even with longer simulation times." @default.
- W2317902007 created "2016-06-24" @default.
- W2317902007 creator A5063362011 @default.
- W2317902007 date "2014-08-13" @default.
- W2317902007 modified "2023-09-23" @default.
- W2317902007 title "Identification of closed-loop human locomotion control with perturbed walking and running data under the constraints of a biologically actuated plant" @default.
- W2317902007 cites W2036407851 @default.
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- W2317902007 doi "https://doi.org/10.6084/m9.figshare.1137192.v2" @default.
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