A New Approach to Human Activity Recognition Using Machine Learning Techniques

摘要

Recognition of human activities aims a wide diversity of applications. However, identifying complicated activities continues a challenging and active research area. In this work, we assess a new approach of feature selection for human activity recognition. For the task, we also compare state-of-the-art classifiers, e.g., Bayes classifier, kNN, MLP, SVM, MLM and MLM-NN. Based on the experiments, the MLM-NN is able to speed up the original MLM while holding equivalent accuracy. MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smartphones.