Performance Analysis of Supervised Machine Learning Algorithms to Recognize Human Activity in Ambient Assisted Living Environment

A significant challenge to provide services to the inhabitant in a smart environment resides in the effective implementation of models. Most of the proposed models are conceptual and lacks practical consideration. Human activity recognition is one of the most challenging tasks to offer the solution for ambient assisted living. In this work, we explore a time series classification problem - human activity recognition. Total of nine machine learning and deep learning algorithms implemented and evaluated using the same dataset. The results are analyzed using different parameters. This paper aims at providing help to select a practical machine learning approach for activity recognition process in ambient assisted living systems. The comparative analysis shows that in deep learning, Long Short-Term Memory (LSTM) network performed best with a classification accuracy of 92%. In machine learning, Logistic Regression and Gradient Boosting came out with a classification accuracy of greater than 90%, and others came out the worst with classification accuracy less than 90%.