Accelerometer-based human activity classification using Water Wave Optimization approach

This paper introduces an approach of classifying accelerometer data for simple human activities using Support Vector Machine (SVM). Classifier results are being optimized by (WWO) Water Wave Optimization Algorithm. Human activity classification has been a hot research point and has been an aid for smart human activity recognition-based systems and for analysis purposes. Numerous classification methods are introduced by researchers including decision trees, bagging of trees, boosting of trees and random forests [1] as well as the SVM that is employed in this work. Accelerometers are very effective source of data for human activity recognition purposes and have been used in many research efforts as well as this paper. This work shows the process of optimizing SVM parameters in order to get better classification results for a set of human activity accelerometer-based data. The applied optimization algorithm is (WWO) [2], a meta-heuristic evolutionary algorithm. 15-Fold cross validation is accomplished with different data preparation using a leave-one-subject-out approach. Classification accuracy on the applied dataset ranges from 81% to 97% for a first run, and approached 100% accuracy for a second run. validation being applied on 15 different folds on 15 separate dataset files each represents a different participant. We employ four different data preparation configurations.