TITLE:
Optimal Features Selection for Human Activity Recognition (HAR) System Using Deep Learning Architectures
AUTHORS:
Subrata Kumer Paul, Rakhi Rani Paul, Md. Atikur Rahman, Md. Momenul Haque, Md. Ekramul Hamid
KEYWORDS:
Surveillance, Optimal Feature, SVM, Complex Tree, Human Activity Recognition, Feature Fusion
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.12,
December
10,
2024
ABSTRACT: One exciting area within computer vision is classifying human activities, which has diverse applications like medical informatics, human-computer interaction, surveillance, and task monitoring systems. In the healthcare field, understanding and classifying patients’ activities is crucial for providing doctors with essential information for medication reactions and diagnosis. While some research methods already exist, utilizing machine learning and soft computational algorithms to recognize human activity from videos and images, there’s ongoing exploration of more advanced computer vision techniques. This paper introduces a straightforward and effective automated approach that involves five key steps: preprocessing, feature extraction technique, feature selection, feature fusion, and finally classification. To evaluate the proposed approach, two commonly used benchmark datasets KTH and Weizmann are employed for training, validation, and testing of ML classifiers. The study’s findings show that the first and second datasets had remarkable accuracy rates of 99.94% and 99.80%, respectively. When compared to existing methods, our approach stands out in terms of sensitivity, accuracy, precision, and specificity evaluation metrics. In essence, this paper demonstrates a practical method for automatically classifying human activities using an optimal feature fusion and deep learning approach, promising a great result that could benefit various fields, particularly in healthcare.