An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods. (2nd February 2022)

• Record Type: Journal Article
• Authors: Ibrahim, Muhammad Junaid; Kainat, Jaweria; AlSalman, Hussain; Ullah, Syed Sajid; Al-Hadhrami, Suheer; Hussain, Saddam
• Other Names: Algalil Fahd Abd Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2022(2022)
• Publication Date: 2022-02-02
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2022/7931729
• Languages: English
• ISSNs: 1176-2322

Abstract

Abstract : Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients' activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.