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Development and Experimental Evaluation of a State Dependent Coefficient Based State Estimator for Functional Electrical Stimulation-Elicited Tasks

zhong, qiang (2016) Development and Experimental Evaluation of a State Dependent Coefficient Based State Estimator for Functional Electrical Stimulation-Elicited Tasks. Master's Thesis, University of Pittsburgh. (Unpublished)

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Functional electrical stimulation (FES) is an application of low-level electrical current to the motor nerves to produce muscle contractions. FES-induced limb motion can be used to reproduce gait in persons with paraplegia. The biggest limitation of using FES for gait restoration is the rapid onset of muscle fatigue. Unlike FES, powered exoskeletons don’t suffer from this limitation but need batteries and large actuators to generate enough torque to restore gait motion. However, a hybrid neuroprosthesis that combines these two technologies may be a promising direction to achieve walking for long durations. Our ultimate goal is to develop a wearable hybrid neuroprosthesis that can be conveniently used in daily life.
In order to use closed loop feedback control for a wearable walking hybrid neuroprosthesis, accurate estimates of lower limb angles need to be determined. This thesis presents a nonlinear estimator that utilized a single joint knee model to estimate the knee joint angle based on wearable sensors. Two inertial measurement units (IMUs) were used to measure kinematic data of the thigh and shank segments. A new class of state estimator called State-Dependent Riccati Equation (SDRE) based estimator was developed to estimate the knee joint angle during FES of the quadriceps muscle. The SDRE estimator is robust to uncertainties in the modeling and sensor bias/noise of the IMUs. To prove that the SDRE estimator is feasible for this application, it was compared with an Extended Kalman Filter (EKF) and a rotation matrix method (RMX). Each estimator's performance was evaluated using a rotary encoder, which was assumed as the true value of the joint angle. The error for each estimator was calculated through the root mean square error (RMSE), in which the experimental results showed that the SDRE estimator had the most accurate knee joint estimation with a mean RMSE of 1.77 degrees. The EKF and rotation matrix gave a mean RMSE of 2.04 degrees and 2.79 degrees, respectively.
Further, a two limb joint angle simulation study was performed to explore the performance of the SDC estimator during multi-DOF limb movements. In another simulation, this novel estimator was combined with the synergy-inspired controller scheme for tracking control of hip and knee joint angles. A discussion on stability analysis of this estimator-controller scheme is also presented in this thesis.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
zhong, qiangqiz55@pitt.eduQIZ55
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSharma, Nitinnis62@pitt.eduNIS62
Committee MemberMao, Zhi-Hongzhm4@pitt.eduZHM4
Committee MemberClark, Williamwclark@pitt.eduWCLARK
Date: 15 June 2016
Date Type: Publication
Defense Date: 4 April 2016
Approval Date: 15 June 2016
Submission Date: 30 March 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 74
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: State-Dependent Riccati Equation, nonlinear estimation, IMU, EKF, FES
Date Deposited: 15 Jun 2016 13:34
Last Modified: 15 Nov 2016 14:32


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