TFG: Deep learning based framework to detect and analyze student behaviours in a classroom
Carlota Vera has successfully presented his final degree project named “Sistema de detección y análisis de emociones y comportamientos de estudiantes en un aula docente basado en Deep learning”.
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| Tutor | Roberto Calvo Palomino |
Artificial intelligence is already helping in the education field to understand the needs of each student and improve the learning methodologies for a better education.
Neural networks such as PoseNet or FaceMesh can help to understand the dynamic of the class without expose the student's privacy. In collaboration with Tknika a deep learning vision-based framework has been developed to detect and analyze emotions, behaviours, movements and talk-action of the students in a regular class.
This work focuses on developing a system for detecting and analyzing emotions and behaviors based on AI on a teaching environment. Main goal of the project is to provide useful information to teachers about student performance. This system detection is based on algorithms that have been designed to perform a visual analysis of images and videos using pose and face neural networks for emotion detection. After all the analysis the information obtained is adapted to use as a guide and help for teachers, who will be able to understand visually the different events that occurred during a class in relation to a certain students. Privacy has been treated as a central pillar in this project, that's why no images are saved, neither audio of the environment is analyzed.
This work focuses on developing a system for detecting and analyzing emotions and behaviors based on AI on a teaching environment. Main goal of the project is to provide useful information to teachers about student performance. This system detection is based on algorithms that have been designed to perform a visual analysis of images and videos using pose and face neural networks for emotion detection. After all the analysis the information obtained is adapted to use as a guide and help for teachers, who will be able to understand visually the different events that occurred during a class in relation to a certain students. Privacy has been treated as a central pillar in this project, that's why no images are saved, neither audio of the environment is analyzed.