Attentive Amigo - Student's alertness Indicator app

In recent situations, the majority of the learning has been moved to e-learning modes which are internet-based classes. But in a live class, an educator can continually screen the understudies by visual examination and dynamic learning. Because of virtual learning, this ability of the educators becomes less efficient. The students are not able to gain enough knowledge as usually they should. The proposed work points towards giving the educator an itemized examination forevery one of the understudies dependent on a physical and passionate investigation of their state during the study hours. Our model analyses live recordings of students and uses factors such as the student's posture, the enthusiastic look on the face, the location of the eyelids, and the student's stance to provide the educator with a certainty score that he or she can use to determine the students' mentality during the class. By knowing which students were attentive and inattentive, the teacher may need to keep a high focus on the inattentive ones. With the effective tecution of our proposed work, it will aid to build up a relationship among the attributes that have been picked and foster this model that will help the instructors to perceive the result of the students, so they can bring better learning techniques nearer to the understudies, while in the security of their own houses, with the help of deep learning and pre-trained data sets to know the behaviour of the students. The utilization of these techniques of mechanically progressed educational strategies and upgraded individual learning examinations will take into consideration the setting up of the labourforce of tomorrow to be exceptionally prepared and able.