Multimodal Approach to Identify Attention Level of Students using Jetson Nano

Students must be attentive in the classroom to improve on their learning. Further, it is important for the faculty to know whether the students pay complete attention in the classroom. If not, faculty can slow down and modify their mode of delivery by making the classes more interesting to gain students attention. The aim of this work is to monitor the student’s attention in the classroom based on their physical state which is the result of their bio chemical and electrical signals in the brain. The approaches like Electro dermal activity (EDA), Electroencephalography (EEG), Electrocardiogram (ECG) are measured by using physiological signals but is hard to extract and analyze. Head bands extracting EEG signals may also distract a student’s attention in classes. The proposed work aims to make use of Deep Convolution Neural Network (DCNN) with Histogram of Gradient (HoG) for face detection and facenet algorithm for face recognition. To evaluate the attentiveness of the students, A Convolution Neural Network model is proposed. The trained model is tested by deploying in Jetson Nano. To identify the attentiveness of the student, the physical state of the face is considered, such as head movement, and the state of eyes and mouth, whether it is opened or closed. Furthermore, the open eye region is analysed if it is partially opened or fully opened. By using CNN to train the model instead of Machine learning algorithms, the accuracy of the system is found to increase.