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All Journal Journal La Multiapp
Huda Jalil Ibrahim
Department of Computer Science, Collage of Sciences, Mustansiriyah University, Iraq
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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Human activity recognition using visibility graph features coupled with machine learning algorithm Huda Jalil Ibrahim; Methaq Taleb Kata; Atheer Yousif Oudah
Journal La Multiapp Vol. 3 No. 6 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i6.755

Abstract

Human activities refer to the actions and behaviors of human beings. These activities can be physical, such as working, playing sports, or playing sports; or mentally, such as learning, problem-solving, or decision-making. Technical development and the emergence of mobile devices such as phones and smart watches, as well as wearable sensors, led to the emergence of many systems to recognize and classify human activities. These systems were developed using the data collected by these devices from a variety of individuals who volunteered to do several activities, such as downstairs, upstairs, sitting, running, standing, and more. Using the WISDM (Wireless Sensor Data Mining) dataset, a new machine learning model is proposed to recognize six different human activities: walking, jogging, going up stairs, going down stairs, sitting, and standing. For signal segmentation, the sliding window technique was used, along with two visibility graph techniques for feature extraction: mean degree and Jaccard coefficient. The Least-Squares Support Vector Machines (LS-SVM) used to classify these activities This model achieves 94% accuracy, demonstrating that the proposed model has a high classification rate.
Co-Authors Atheer Yousif Oudah Methaq Taleb Kata