Human activity recognition using visibility graph features coupled with machine learning algorithm

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.