Authors: P.S. Prakash; S. Balakrishnan; K. Venkatachalam; Saravana Balaji Balasubramanian
Addresses: Department of Computer Science and Engineering, Vivekanandha College of Engineering for Women, Tiruchengode, Namakkal, 637205, India ' Department of Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India ' Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 50003 Hradec Králové, Czech Republic ' Department of Information Technology, Lebanese French University, Erbil, Iraq
Abstract: Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%.
Keywords: convolutional neural network; CNN; people activity identification; random forest.
International Journal of Cloud Computing, 2023 Vol.12 No.2/3/4, pp.191 - 200
Received: 23 Jan 2020
Accepted: 07 Apr 2020
Published online: 14 May 2023 *