Conceptual exploration and comparative study on the use of deep learning approach in HAR models
Abdulrahman S. Mohialdeen, Baraa Munqith Albaker, Malik Abdulrazzaq Alsaedi · AIP conference proceedings · 2022
Human Activity Recognition (HAR) has taken great attention from researchers last few years, because of the promising results shown by deep learning, and the necessity to make a recognizer system, in this paper a comparison between two types of Convolutional Neural Network (CNN) architectures will be presented. Two Dimensional (2D) CNN followed by a Recurrent Neural Network (RNN) referring to it as 2D-CNN-RNN, and 3D-CNN. Filter with 3D-CNN will be used, after training and testing the models with two different datasets, KTH which has six human activities (Boxing, Handclapping, Handwaving, Walking, Jogging, and Running), and UT-Interaction dataset that has six interaction activities (Handshake, Hug, Kick, Point, Punch, and Push). 3D-CNN shown remarkable results with the aid of filter, but without filter, the dominant was 2D-CNN-RNN models.