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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/29390
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dc.contributor.authorMatira, Kenneth-
dc.date.accessioned2024-01-15T18:06:44Z-
dc.date.available2024-01-15T18:06:44Z-
dc.date.issued2023-12-
dc.identifier.urihttp://hdl.handle.net/11375/29390-
dc.description.abstractIn the world of Virtual Reality (VR), motion sickness, nausea, and disorientation remains a big concern for many users. The KAT Walk C is an omni-directional treadmill, which aims to convert human motion to virtual movement. This is intended to reduce the aforementioned concerns. However, the original KAT C algorithm of human locomotion has its limitations, where motion is frequently converted incorrectly. In this report, we will introduce an alternative input mechanism for the KAT Walk C, KATNN, which focuses on two primary objectives: allowing the user to move in multiple directions, and having the ability to register slower type motions. KATNN was created by the construction of modular neural networks. We will discuss steps to create the models, investigate current issues and potential solutions involving calibration and disorientation. Readers may optionally view the results by watching the following video: https://youtu.be/SbUXoQ0-G9Qen_US
dc.language.isoenen_US
dc.publisherDepartment of Computing and Software, McMaster Universityen_US
dc.rightsAn error occurred on the license name.*
dc.rights.uriAn error occurred getting the license - uri.*
dc.subjectunityen_US
dc.subjectmachine learningen_US
dc.subjectvirtual realityen_US
dc.subjectmotion captureen_US
dc.subjectneural netwworksen_US
dc.subjectlocomotionen_US
dc.subjectKATVRen_US
dc.titleKATNN: KAT Walk C Alternative Motion Capture Algorithmen_US
dc.title.alternativeKATNN: Motion Capture and Machine Learning to Predict Realistic Character Trajectory in a Virtual Game for the KAT Walk Cen_US
dc.typeArticleen_US
dc.contributor.departmentComputing and Softwareen_US
Appears in Collections:Student Publications (Not Graduate Theses)

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