Human Activity Analysis using Machine Learning Classification Techniques

Abstract

In recent times, smart phones are playing a vital role to recognize the human activities and became a well-known field of research. Detail overview of various research papers on human activity recognition are discussed in this paper. Artificial Intelligence(AI) models are developed to recognize the activity of the human from the provided UCI online storehouse. The data chosen is multivariate and we have applied various machine classification techniques Random Forest, kNN, Neural Network, Logistic Regression, Stochastic Gradient Descent and Naïve Bayes to analyse the human activity. Besides building AI models, the dimension of the dataset is reduced through feature selection process.Precision and Recall values were calculated and a Confusion Matrix for each model was made. Experiment results proved that the Neural Network and logistic regression provides better accuracy for human activity recognition compared to other classifiers such as k-nearest neighbor (KNN), SGD , Random Forest and Naïve Bayes though they take higher computational time and memory resources.