03. Bachelor's Thesis
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Item Open Access ADAPTING TO LEARNER’S COGNITIVE DIFFERENCES USING REINFORCEMENT LEARNING(Nazarbayev University School of Engineering and Digital Sciences, 2023-05-02) Nurgazy, Symbat; Issa, Ilyas; Kassymbekov, Saparkhan; Kuangaliyev, ZholamanThis report explores the benefits and challenges of creating adaptive robotic systems by integrating open-source software and reinforcement learning algorithms. The aim is to develop robotic systems that adapt to cognitive differences of the child to increase the engagement time and develop social skills using robot-assisted therapy. The Furhat and NAO robots are used as the platforms for receiving inputs and outputs, while a reinforcement learning agent selects the order of ac- tivities. The state space includes the pose landmarks from the Mediapipe and OpenPose libraries to recognize the engagement level and some static parameters such as age, autism level, verbality, and the co-diagnosis of Attention Deficit Hyperactivity Disorder. The actions space contains the act of changing the current activity. In the current stage, we have eight types of activities the robot choose to interact with the child. Furhat robot is programmed to have a full pipeline for a conversational robot: automatic speech recognition, text-to-speech, machine translation, and OpenAI language model. The results of this project provide insight into the potential of using open-source software and reinforcement learning algorithms to create more advanced interactive robots and highlight the importance of continued research in the field of Human-Robot Interaction.Item Open Access DEVELOPMENT OF BRAIN-BASED SMART-HOME/TYPING SYSTEM(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-19) Yergaliyeva, Aiana; Berikbol, Arnur; Seiilkhan, ArsenOur project combines EEG-EOG signals to develop an efficient Brain-Computer Interface (BCI) spelling system for Virtual Reality (VR) and Mixed Reality (MR) environments. This hybrid speller enables users to spell using brain activity by leveraging multi-modal signals and various classification strategies. Aimed at improving the quality of life for individuals with motor disabilities, such as spinal cord injuries, ALS, locked-in syndrome, and the elderly, our BCI system provides an alternative communication channel. Focusing on the well-established P300 Row-Column (RC) speller paradigm, we incorporate convolutional neural network (CNN) classification techniques for enhanced performance. Additionally, we use mixed reality glasses to improve user comfort and EEG signal quality. Our methodology includes comprehensive experimental procedures, from environment setup to data analysis and iterative refinement. By advancing BCI technology and integrating VR and MR interfaces, our project seeks to promote accessibility and inclusivity, enabling individuals of all abilities to communicate and participate more fully in social, educational, and professional activities.