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Using a Knowledge Engineering Framew...

Rich, Addison John.

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Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke./
Author:	Rich, Addison John.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	183 p.
Notes:	Source: Masters Abstracts International, Volume: 81-08.
Contained By:	Masters Abstracts International81-08.
Subject:	Biomedical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27541493
ISBN:	9781392819739

Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke.
Rich, Addison John.

Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 183 p.

Source: Masters Abstracts International, Volume: 81-08.

Thesis (M.A.S.)--University of Toronto (Canada), 2019.

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

This thesis presents preliminary studies in developing clinical reasoning capabilities for an autonomous, intelligent, post-stroke upper-extremity rehabilitation hybrid therapy system. The hybrid system combines functional electrical stimulation (FES) with robotics, which allows the hybrid system to monitor a patient's performance during therapy while using its clinical reasoning capabilities to intelligently decide when to intervene with FES during a therapy session so as to be most beneficial to the patient. A novel approach to develop the hybrid system's clinical reasoning capabilities is taken by applying a systematic Knowledge Engineering (KEG) framework to elicit tacit knowledge of four experienced physical and occupational therapists, and explicate it into a knowledge model. A prototype of the hybrid system is then demonstrated to three occupational therapists via a mock therapy session. The KEG framework generated a sufficient amount of knowledge for implementation in the hybrid system software, while therapists agreed with the FES control decisions.

ISBN: 9781392819739Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Functional Electrical Stimulation

Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke.
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300 $a 183 p.
500 $a Source: Masters Abstracts International, Volume: 81-08.
500 $a Includes supplementary digital materials.
500 $a Advisor: Mihailidis, Alex;Wang, Rosalie.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2019.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis presents preliminary studies in developing clinical reasoning capabilities for an autonomous, intelligent, post-stroke upper-extremity rehabilitation hybrid therapy system. The hybrid system combines functional electrical stimulation (FES) with robotics, which allows the hybrid system to monitor a patient's performance during therapy while using its clinical reasoning capabilities to intelligently decide when to intervene with FES during a therapy session so as to be most beneficial to the patient. A novel approach to develop the hybrid system's clinical reasoning capabilities is taken by applying a systematic Knowledge Engineering (KEG) framework to elicit tacit knowledge of four experienced physical and occupational therapists, and explicate it into a knowledge model. A prototype of the hybrid system is then demonstrated to three occupational therapists via a mock therapy session. The KEG framework generated a sufficient amount of knowledge for implementation in the hybrid system software, while therapists agreed with the FES control decisions.
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653 $a Knowledge Engineering
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