Vision par ordinateur pour tp electronique a distan. Enriching remote learning labs using computer vision and pattern recornigtion techniques
- Autores: Fawzi KHATTAR
- Directores de la Tesis: Fadi Dornaika (dir. tes.), Franck Luthon (dir. tes.), Blanca Rosa Cases Gutiérrez (dir. tes.)
- Lectura: En la Universidad del País Vasco - Euskal Herriko Unibertsitatea ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: Franck Luthon (presid.), Fadi Dornaika (secret.), Yassine Ruichek (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Informática por la Universidad del País Vasco/Euskal Herriko Unibertsitatea
- Materias:
- Enlaces
- Tesis en acceso abierto en: ADDI
- Resumen
With the technological advance, new learning technologies are being developed in order to contribute to better learning experience. In particular, remote labs constitute an interesting and a practical way that can motivate nowadays students to learn. The studen can at anytime, and from anywhere, access the remote lab and do his lab-work. Despite many advantages, remote tecnologies in education create a distance between the student and the teacher. Without the presence of a teacher, students can have difficulties, if no appropriate interventions can be taken to help them. In this thesis, we aim to enrich an existing remote electronic lab made for engineering students called "LaboREM" (for remote Laboratory) in two ways: first we enable the student to send high level commands to a mini-drone available in the remote lab facility. The objective is to examine the front panels of electronic measurement instruments, by the camera embedded on the drone. Furthermore, we allow remote student-teacher communication using the drone, in case there is a teacher present in the remote lab facility. Finally, the drone has to go back home when the mission is over to land on a platform for automatic recharge of the batteries. Second, we propose an automatic system that estimates the affective state of the student (frustrated/confused/flow) in order to take appropriate interventions to ensure good learning outcomes. For example, if the studen is having major difficulties we can try to give him hints or to reduce the difficulty level of the lab experiment. We propose to do this by using visual cues (head pose estimation and facil expression analysis). Many evidences on the state of the student can be acquired, however these evidences are incomplete, sometims inaccurate, and do not cover all the aspects of the state of the student alone. This is why we propose to fuse evidences using the theory of Dempster-Shafer that allows the fusion of incomplete evidence.
