Abstract
Human Activity Recognition (HAR) has become an active field of research in the computer vision community. Recognizing the basic activities of human beings with the help of computers and mobile sensors can be beneficial for numerous real-life applications. The main objective of this paper is to recognize six basic human activities, viz., jogging, sitting, standing, walking and whether a person is going upstairs or downstairs. This paper focuses on predicting the activities using a deep learning technique called Convolutional Neural Network (CNN) and the accelerometer present in smartphones. Furthermore, the methodology proposed in this paper focuses on grouping the data in the form of nodes and dividing the nodes into three major layers of the CNN after which the outcome is predicted in the output layer. This work also supports the evaluation of testing and training of the two-dimensional CNN model. Finally, it was observed that the model was able to give a good prediction of the activities with an average accuracy of 89.67%. Considering that the dataset used in this research work was built with the aid of smartphones, coming up with an efficient model for such datasets and some futuristic ideas pose open challenges in the research community.
Highlights
Published: 19 December 2021The accurate measurement of daily activities performed by people has gathered attention for both researchers and the gadget industries
The goal of this research is to recognize or predict the action performed by a human subject using certain specialized sensors capable of recording related data [1]
The data recorded in such a manner are advantageous for many industries and technology giants as it helps them in directing their research and development towards a product that can be potentially launched in the Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Summary
The accurate measurement of daily activities performed by people has gathered attention for both researchers and the gadget industries. This has given rise to the subject matter of Human Activity Recognition (HAR). Smart bands, cell phones and smartphones, are handy pieces of equipment to identify and analyze what a person is doing. Such gadgets provide a wide spectrum of sensors that can be used with ease in day-to-day life with stellar performance and high accuracy. The data recorded in such a manner are advantageous for many industries and technology giants as it helps them in directing their research and development towards a product that can be potentially launched in the Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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