Human Activity Recognition (HAR) is a research area that is receiving increasing attention in recent years. In this paper we propose the application of different supervised learning algorithms to recognize distinct human activities. In particular, we use a dataset that includes inertial measurements recorded from sensors placed in various positions on the subjects' body, while performing sports and daily activities. Considering possible real-life applications of the system, we analyze only the acceleration signal coming from a single and low-complexity sensor placed on the torso of the subjects. We derive different statistical features from the three axial accelerations. These features are the input of Machine Learning algorithms with the purpose of recognizing the particular activity carried out by the subjects. The unprocessed acceleration signals are instead sent to Deep Learning algorithms, giving us the opportunity to compare the performance of the classifiers. In the end, we achieve accuracy values of 73.3% and 86.6% in classifying 19 types of different human activities, using a Random Forest (RF) classifier and a 1D Convolutional Neural Network (CNN) network, respectively.
