Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Control Methods for Compensation and Inhibition of Muscle Fatigue in Neuroprosthetic Devices

Kirsch, Nicholas (2016) Control Methods for Compensation and Inhibition of Muscle Fatigue in Neuroprosthetic Devices. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Primary Text

Download (9MB)

Abstract

For individuals that suffer from paraplegia activities of daily life are greatly inhibited. With over 5,000 new cases of paraplegia each year in the United States alone there is a clear need to develop technologies to restore lower extremity function to these individuals. One method that has shown promise for restoring functional movement to paralyzed limbs is the use of functional electrical stimulation (FES), which is the application of electrical stimulation to produce a muscle contraction and create a functional movement. This technique has been shown to be able to restore numerous motor functions in persons with disability; however, the application of the electrical stimulation can cause rapid muscle fatigue, limiting the duration that these devices may be used. As an alternative some research has developed fully actuated orthoses to restore motor function via electric motors. These devices have been shown to be capable of achieving greater walking durations than FES systems; however, these systems can be significantly larger and heavier. To develop smaller and more efficient systems some research has explored hybrid neuroprostheses that use both FES and electric motors. However, these hybrid systems present new research challenges.

In this dissertation novel control methods to compensate/inhibit muscle fatigue in neuroprosthetic and hybrid neuroprosthetic devices are developed. Some of these methods seek to compensate for the effects of fatigue by using fatigue dynamics in the control development or by minimizing the amount of stimulation used to produce a desired movement. Other control methods presented here seek to inhibit the effects of muscle fatigue by adding an electric motor as additional actuation. These control methods use either switching or cooperative control of FES and an electric motor to achieve longer durations of use than systems that strictly use FES. Finally, the necessity for the continued study of hybrid gait restoration systems is facilitated through simulations of walking with a hybrid neuroprosthesis. The results of these simulations demonstrate the potential for hybrid neuroprosthesis gait restoration devices to be more efficient and achieve greater walking durations than systems that use strictly FES or strictly electric motors.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Kirsch, Nicholasnak65@pitt.eduNAK650000-0002-0894-1580
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSharma, Nitinnis62@pitt.eduNIS62
Committee MemberCole, Danieldgcole@pitt.eduDGCOLE
Committee MemberClark, Williamwclark@pitt.eduWCLARK
Committee MemberVipperman, Jeffreyjsv@pitt.eduJSV
Committee MemberMao, Zhi-Hongmaozh@pitt.eduMAOZH
Committee MemberRedfern, Markmredfern@pitt.eduMREDFERN
Date: 15 June 2016
Date Type: Publication
Defense Date: 21 March 2016
Approval Date: 15 June 2016
Submission Date: 23 March 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 175
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Neuroprosthesis, Functional Electrical Stimulation, Musculoskeletal Modeling, Nonlinear Controls, Optimal Control, Model Predictive Control, Dynamic Optimization
Date Deposited: 15 Jun 2016 19:34
Last Modified: 15 Nov 2016 14:32
URI: http://d-scholarship.pitt.edu/id/eprint/27316

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item