Gimalay, Ibragim2024-06-232024-06-232024-04-19Gimalay, I. (2024). Application of Deep neural networks and computer vision in rehabilitation robots. Nazarbayev University School of Engineering and Digital Scienceshttp://nur.nu.edu.kz/handle/123456789/7957The objective of this research is to develop an automated system for detecting gait-related health issues using Deep Neural Networks (DNNs). The system processes video footage of patients to estimate their 3D body posture through a DNN-based method, then this 3D body posture gets classified using another DNN-based method. The analyzed 3D body pose data is classified into 3 categories: Healthy, Parkinson’s disease and Post Stroke. This technology eliminates the need for bulky, complex equipment and extensive lab space, making it practical for use at home. It also doesn't require specialized knowledge for feature engineering, as it automatically extracts meaningful, high-level features from the data. The test results show classification accuracies ranging from 56% to 96% across different groups. The conclusion of this study indicates that this system is a promising tool for automatically classifying gait disorders and could be a foundational technology for future deep learning applications in clinical gait analysis. The significance of this system is underscored by its use of digital cameras as the sole required equipment, facilitating its use in patient homes and among the elderly for regular monitoring and early detection of gait changes.enAttribution-NonCommercial-NoDerivs 3.0 United StatesType of access: EmbargoDNNRehabilitationComputer visionBachelor's thesis