Improving the Performance of an Activity Recognition System Using Meaningful Data Augmentation and Deep Learning Methods, M.Sc. Thesis Sharif University of Technology ; Behzadipour, Saeed (Supervisor)
Abstract
Researchers working at Mowafaghian Rehabilitation Research Center have decided to develop a telerehabilitation system named SEPANTA, especially designed for activity recognition of Parkinson's Disease patients. In this regard, the system uses 34 mobility exercises, including 20 LSVT-BIG activities (especially designed for PD patients) and 14 functional daily activities. Human Activity Recognition (HAR) systems faces various challenges e.g., intra-class variabilities, meaning differences in an activity performance by different persons or a person. Data augmentation and utilizing deep learning models are the most common solutions for the risen challenges. However, deep structures require an...
Cataloging briefImproving the Performance of an Activity Recognition System Using Meaningful Data Augmentation and Deep Learning Methods, M.Sc. Thesis Sharif University of Technology ; Behzadipour, Saeed (Supervisor)
Abstract
Researchers working at Mowafaghian Rehabilitation Research Center have decided to develop a telerehabilitation system named SEPANTA, especially designed for activity recognition of Parkinson's Disease patients. In this regard, the system uses 34 mobility exercises, including 20 LSVT-BIG activities (especially designed for PD patients) and 14 functional daily activities. Human Activity Recognition (HAR) systems faces various challenges e.g., intra-class variabilities, meaning differences in an activity performance by different persons or a person. Data augmentation and utilizing deep learning models are the most common solutions for the risen challenges. However, deep structures require an...
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