Human Activity Recognition implenting the Yolo models

Human activity recognition (HAR) provides information about interest situations, in this way a follow-up of the activities carried out is obtained, this is applied to areas in health such as the monitoring or diagnosis of diseases for vulnerable people, in video surveillance by detecting suspicious acts in certain places, in human-machine interaction, the assistance of robots with people, to mention a few examples.This article will address the implementation of a convolutional neural network (CNN) architecture for the identification of activities interest, which is part of a multimodal human activity recognition system.