Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network

Applied Sciences

Maha M. Althobaiti¹  Ahmed Almulihi¹  Romany F. Mansour²  Ayman M. Mahmoud²  Ashwani Prasad³  Amit Kumar Tyagi³ 

[1] Department of Computer Science, College of Computing and Information technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt;School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India;

关键词: human activity recognition;    sensors;    accelerometer;    cell phones;    dataset;    deep learning;   

DOI  :  10.3390/app112412099

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown