Comparison of the Predictive Models of Human Activity Recognition (HAR) in Smartphones

Abstract

This report compared the performance of different classification algorithms such as decision tree, K-Nearest Neighbour (KNN), logistic regression, Support Vector Machine (SVM) and random forest. The dataset comprised smartphones’ accelerometer and gyroscope readings of the participants while performing different activities, such as walking, walking downstairs, walking upstairs, standing, sitting, and laying. Different machine learning algorithms were applied to this dataset for classification and their accuracy rates were compared. KNN and SVM were found to be the most accurate of all. KEYWORDS— decision tree, Human Activity Recognition (HAR), K-Nearest Neighbour (KNN), logistic regression, random forest, Support Vector Machine (SVM)