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Advancing Medical Technology for Mot...

Yough, Matthew.

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Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices./
Author:	Yough, Matthew.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	185 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Motor ability. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30725982
ISBN:	9798381018011

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices.
Yough, Matthew.

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 185 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--West Virginia University, 2023.

This item must not be sold to any third party vendors.

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson's Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology.In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a highdensity electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles.

ISBN: 9798381018011Subjects--Topical Terms:

548676
Motor ability.

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices.
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500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Gritsenko, Valeriya.
502 $a Thesis (Ph.D.)--West Virginia University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson's Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology.In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a highdensity electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles.
590 $a School code: 0256.
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