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- W3155729039 abstract "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.%%%%M.A.S." @default.
- W3155729039 created "2021-04-26" @default.
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- W3155729039 date "2019-11-01" @default.
- W3155729039 modified "2023-09-26" @default.
- W3155729039 title "Using a Knowledge Engineering Framework to Develop the Clinical Reasoning of a Hybrid Therapy System for Upper Extremity Rehabilitation after Stroke" @default.
- W3155729039 hasPublicationYear "2019" @default.
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