Human activity recognition using ensemble machine learning classifiers.

Activity recognition offers a wide range of applications in various industrial processes and healthcare. This work proposes an approach to collect data from a spherical coordinate system using smartphones, then extract the highly efficient features using advanced preprocessing. The paper also proposes an algorithm to recognize activity using various ensemble machine-learning approaches based on extracted features. These approaches are evaluated under various combinations of features to analyze the accuracy, sensitivity, specificity, and training time. Experimental results reveal that weighted KNN performs best among all models by achieving 96.2% accuracy with 12 features. On the other hand, Bagged tree ensemble classifiers perform better than subspace KNN ensemble classifiers with an accuracy of 95.3% using 12 features. [ABSTRACT FROM AUTHOR]