Human Activity Recognition for Online Examination Environment Using CNN

Abstract

Human Activity Recognition (HAR) is an intelligent system that recognizes activities based on a sequence of observations about human behavior. Human activity recognition is essential in human-to-human interactions to identify interesting patterns. It is not easy to extract patterns since it contains information about a person’s identity, personality, and state of mind. Many studies have been conducted on recognizing human behavior using machine learning techniques. However, HAR in an online examination environment has not yet been explored. As a result, the primary focus of this work is on the recognition of human activity in the context of an online examination. This work aims to classify normal and abnormal behavior during an online examination employing the Convolutional Neural Network (CNN) technique. In this work, we considered two, three and four layered CNN architectures and we fine-tuned the hyper-parameters of CNN architectures for obtaining better results. The three layered CNN architecture performed better than other CNN architectures in terms of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.