Machine Learning Models for Human Activity Recognition: A Comparative Study

With the advancements in machine learning, human activity recognition has found its applications in several emerging areas such as robotics healthcare, surveillance, smart environment etc. This paper aims to study and evaluate the performance of some popularly used machine learning algorithms in classifying human activities. We have selected K-NN, SVM and XGBoost methods in this study and the performance of the methods has been evaluated for 19 different activities which were performed by eight random persons. The required data was recorded using 5 MTx 3-DOF orientation trackers. The raw data was processed before feature extraction and then fed as input to the machine learning models. On performance comparison of these methods, it has been found that the SVM method when implemented with a polynomial kernel, outperforms the other state-of-the-art methods. It classified the different activities with an accuracy of 96.9%.