Realtime Determination of Mood in Classroom Environment

Many innovations have occurred with today's technologies, the issue of detecting and analyzing human behavior through image processing techniques has also gained popularity recently. However, studies carried out with image processing in the field of education are not very common and many students' interests in class courses are unknown. Aim of this study is to track students in a classroom via webcam to detect, track their faces and create a system that can detect moments when the teacher cannot determine the condition of the student. For this purpose, a Python application was developed using OpenCV libraries Electrocardiography (ECG) and Galvanic Skin Response (GSR) was observed through monitoring heart rate and GSR. A real-time system was obtained by analyzing our ECG/GSR and heart rate sensor data, which we received in Python, via Arduino. Using the face, heartbeat sensor, ECG, and GSR, students' moods were determined in real time, and by developing this in Arduino, it was able to reflect students' attentional states analyzed in real time with colored LED lights.