Machine Learning Approaches for Human Activity Recognition Based on Multimodal Body Sensors

Abstract

Human Activity Recognition (HAR) has become a subject of high interest within many fields. In this work, the target is to build a classifier with high accuracy, so two approaches are used to build a classifier. The first approach represented by model (A), and the second represented by the model (B), both models are built using four machine learning classification algorithms, which are Random Forest (RF), Naïve Bayes (NB), Support Vector Machines (SVM), and Multilayer perceptron (MLP). Model (A) depends on a single classifier for recognizing 12 activities and Model (B) depends on applying the ensemble technique and build in two steps for recognizing the same activities. The experimental comparison result shows that model (B) improved the accuracy of model (A), as the accuracy is increased with 3.6% in Random Forest, 4.7% in Multilayer perceptron, and 23.2% in Naïve Bayes but for Support Vector Machines, model (A) achieved higher accuracy than model (B).