Calibrating Video Capture Systems To Aid Automated Analysis And Expert Rating Of Human Movement Performance

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dc.contributor.authorYeshala, Sai krishnaen
dc.contributor.committeechairKelliher, Aislingen
dc.contributor.committeememberGracanin, Denisen
dc.contributor.committeememberEllis, Margaret O.'Neilen
dc.contributor.committeememberRikakis, Thanassisen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2022-06-28T08:00:30Zen
dc.date.available2022-06-28T08:00:30Zen
dc.date.issued2022-06-27en
dc.description.abstractWe propose a methodology for calibrating the activity space and the cameras involved in video capture systems for upper extremity stroke rehabilitation. We discuss an in-home stroke rehabilitation system called Semi-Automated Rehabilitation At Home System (SARAH) and a clinic-based system called Action Research Arm Test (ARAT) developed by the Interactive Neuro-Rehabilitation Lab (INR) at Virginia Tech. We propose a calibration workflow for achieving invariant video capture across multiple therapy sessions. This ensures that the captured data is less noisy. In addition, there is prior knowledge of the captured activity space and patient location in the video frames provided to the Computer Vision algorithms analyzing the captured data. Such a standardized calibration approach improved machine learning analysis of patient movements and a higher rate of agreement across multiple therapists regarding the captured patient performance. We further propose a Multi-Camera Calibration approach to perform stereo camera calibration in SARAH and ARAT capture systems to help perform a 3D reconstruction of the activity space from 2D videos. The importance of the proposed activity space and camera calibration workflows, including new research paths opened as a result of our approach, are discussed in this thesis.en
dc.description.abstractgeneralIn this thesis, I describe the workflows I developed to perform calibration of stroke rehabilitation activity spaces, including the calibration of cameras involved in video capture systems for analyzing patient movements in stroke rehabilitation practices. The proposed workflows are designed to facilitate convenient user involvement in calibrating the video capture systems to provide invariant and consistent video captures, including the extraction of fine-grain information utilizing camera calibration results, to the therapists and computer vision-based automated systems for improved analysis of patient performance in stroke rehabilitation practices. The importance of human-in-the-loop systems, including future research paths to strengthen the symbiotic relationship between humans and Artificial Intelligence systems in stroke rehabilitation practices, is discussed. The quantitative and qualitative results generated from the workshops conducted to test and evaluate the calibration workflows align with the stakeholder's needs in stroke rehabilitation systems.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:35226en
dc.identifier.urihttp://hdl.handle.net/10919/110949en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectVideo capture systemsen
dc.subjectMulti-camera calibrationen
dc.subjectActivity space calibration.en
dc.titleCalibrating Video Capture Systems To Aid Automated Analysis And Expert Rating Of Human Movement Performanceen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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