Human Activity Identification using CNN

Abstract

Human activity recognition [HAR] is a field of study that deals with identifying, interpreting, and analyzing the actions specific to the movement of human beings. Currently, the activity recognition system like (HAR) is becoming a huge field of innovative work with an emphasis on advanced machine learning algorithms, innovations that focus on increasing safety while decreasing the costs of monitoring, which helps in the field of healthcare, child care, surveillance, sports or keeping track of behavioral pattern of human beings. This model aims to develop a system that recognizes activities like sitting, standing, walking, sleeping, reading, and tilting using CNN. It is done by a supervised learning method, which is an ML task where a function is trained that provides output by mapping it to input, i.e., the activity will be recognized based on the activity defined/labeled in the data.