Sheng, Zhiyu
(2021)
Ultrasound Imaging for Assessment of Muscle Contractility and Estimation of FES-Induced Fatigue in a Hybrid Neuroprosthesis.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
A quick onset of muscle fatigue is a critical challenge during functional electrical stimulation (FES) for motor function restoration after neurological injuries such as spinal cord injury and stroke. Due to torque reducing FES-induced muscle fatigue effects, it is usually challenging to sustain desired limb movements with FES. A hybrid neuroprosthesis that uses exoskeleton assistance in addition to FES can compensate for the muscle fatigue effects. However, for its optimal control, direct measurement of muscle fatigue as feedback information is needed.
Conventional strategies for fatigue estimation are based on either indirect predictive models or traditional sensor modalities such as tetanic force measurements, electromyography (EMG), surface electromyography (sEMG), near-infrared spectroscopy (NIRS) and phosphorus nuclear magnetic resonance (NMR). More recent studies have proposed ultrasound imaging as the alternative. Ultrasound imaging is advantageous in multiple aspects, e.g., it is highly viable and largely established in clinical practice, has a relatively wide in depth field of view (FOV), and is capable of in vivo collecting 2-dimensional (2D) information of a specific targeted muscle. However, the change of the muscle contractility detected by ultrasound imaging during FES has never been studied. The correlation between the images and the contractile force is unknown. Signal processing algorithms are also required so that ultrasound imaging-derived information can be appropriately extracted and deployed to be used in a hybrid neuroprosthesis.
By answering these research questions, the primary goal of this dissertation is to develop a novel non-invasive ultra-high-frame-rate ultrasound imaging technology for muscle contractility assessment and fatigue estimation in a lower-limb hybrid neuroprosthesis. A general theoretical framework of an N-degree-of-freedom (N-DOF) hybrid neuroprosthesis model is formulated under a switched control of the inputs from both FES and electrical motors. Algorithms for appropriately processing the ultrasound images are designed. Methodologies for estimating the FES-induced fatigue through changes in muscle contractility are established. The fulfillment of this dissertation contributes to investigate the imperative precursor of a novel lower-limb hybrid neuroprosthesis that acquires muscle information to assist the decision making in controlling the FES and electrical motors for fatigue compensation.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
26 January 2021 |
| Date Type: |
Publication |
| Defense Date: |
29 October 2020 |
| Approval Date: |
26 January 2021 |
| Submission Date: |
5 November 2020 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
146 |
| 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: |
ultrasound imaging, control theory, hybrid neuroprosthesis, muscle, FES, NMES |
| Date Deposited: |
26 Jan 2021 17:38 |
| Last Modified: |
26 Jan 2022 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/39848 |
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