Human Activity Recognition Using LSTM Architecture

ABSTRACT

The covid 19 pandemic has made everything virtual, including education. It is difficult to tell if students are focused or not due to online education. To help teachers, we are developing a framework for recognizing and assessing student focus. By using the concept of brain-computer communication, we can find the student's concentration level. The data obtained from the electroencephalogram (EEG) signals is used as a data set to predict concentration levels. A four-channel device is used to capture brain waves. The data were preprocessed and feature extraction was performed to determine the concentration level as active or inactive. In this method, we use a multiclass approach to develop a deep learning model that uses LSTM to classify concentration into low, or high concentration levels with accuracy of 88%. © 2022 IEEE.