A Comparative Study on Machine Learning Classification Models for Activity Recognition

Abstract

Activity Recognition (AR) systems are machine learning models developed for cell-phones and smart wearables to recognize various real-time human activities such as walking, standing, running and biking. In this paper, the performance (accuracy and computational time) of several well-known supervised and unsupervised learning models including Logistic Regression, Support Vector Machine, K-Nearest Neighbors’, Naive Base, ’Decision Tree’ and Random Forest are examined on a dataset. It is shown that Random Forest model outperforms other models with accuracy over 99 percent. It is shown that PCA significantly improved the performance of Artificial Neural Network with one hidden layer and SVM models in both accuracy and time, while PCA showed to have negative impacts on Random Forest or Decision Tree models by increasing the running time and decreasing the prediction accuracy.