Human Activity Recognition using CNN and Pretrained Machine Learning Models

Abstract

People's lives are enriched by human activity recognition (HAR), which extracts action-level details about human behaviour from raw input data. There are a variety of uses for Human Activity Recognition, including elderly surveillance systems, abnormal behaviour, and so forth. However, deep learning models such as Convolutional Neural Networks have outperformed traditional machine learning methods. As a result of CNN, it is possible to extract features and reduce computational costs. Transfer Learning, on the other hand, refers to the use of pre-trained machine learning models that can be used to detect human activity using a special type of Artificial Neural Network known as Leveraging CNN. Resnet-34 Use of a CNN model can provide detection accuracy up to 96.95 percent for human activity recognition Numerous studies and research have been conducted on HAR. There are only a few models in the majority of the paper, however What we know is that the more data we have, the better the model and the more accurate the model will be.