Event Title
Virtual Reality-Robotic Walking Training Device
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Faculty Mentor
Dr. Wathiq Ibrahim
Document Type
Poster Presentation
Date of Publication
2021
Abstract
The purpose of the VR-RWTD is to build and optimize a training device that accurately mimics walking gaits of the ankle and knee for patients who are recovering from spinal cord injuries (S.C.I) or stroke side effects. The motivation behind this project is to enhance the rehabilitation process for patients who have lost lower limb motor control by creating a faster and safer training device that boosts the overall morale of the patient. This being the 4th generation, design specifications yield implementing a Virtual Reality (VR) world and creating motor code that produces gaits within 90% accuracy of empirically captured motion data. By using the Unreal gaming engine, the VR world generated was a forest design that allows the patient to roam freely in a forest full of trees, tall grass and rocks. The motor code was developed in Arduino to control a single motor and was shown to prove an accuracy for both gaits within 90% using feedback data from the motor. To further progress this project, future work includes producing code to control five additional motors and incorporating a mechanism that produces an accurate hip gait.
Keywords
Virtual Reality, Walking Training
Persistent Identifier
http://hdl.handle.net/10950/3032
Ferrero_Poster
Virtual Reality-Robotic Walking Training Device
The purpose of the VR-RWTD is to build and optimize a training device that accurately mimics walking gaits of the ankle and knee for patients who are recovering from spinal cord injuries (S.C.I) or stroke side effects. The motivation behind this project is to enhance the rehabilitation process for patients who have lost lower limb motor control by creating a faster and safer training device that boosts the overall morale of the patient. This being the 4th generation, design specifications yield implementing a Virtual Reality (VR) world and creating motor code that produces gaits within 90% accuracy of empirically captured motion data. By using the Unreal gaming engine, the VR world generated was a forest design that allows the patient to roam freely in a forest full of trees, tall grass and rocks. The motor code was developed in Arduino to control a single motor and was shown to prove an accuracy for both gaits within 90% using feedback data from the motor. To further progress this project, future work includes producing code to control five additional motors and incorporating a mechanism that produces an accurate hip gait.