Multi-Objective Hierarchical Classification Using Wearable Sensors in a Health Application

This paper introduces a novel multi-classification technique, which improves two conflicting main objectives of classification problems, i.e., classification accuracy and worst case sensitivity. Global performance measures such as overall accuracy might not be enough to evaluate classifiers and alternative measurements are essentially required. This paper addresses a new model selection problem to construct a tree-based hierarchical classification model based on ensemble of six different classifiers. In our proposed approach, the model selection is tackled as a multi-objective optimization, which not only considers the accuracy of the classification, but also tries to maximize the worst case sensitivity of the multi-class problem. The proposed technique is applied on nine different classes corresponding to various breathing disorders for designing a wearable remote monitoring system. This model correctly classified the respiratory patterns of ten subjects with an accuracy of 99.25% and a sensitivity of 97.78% with detecting the changes in the anterior-posterior diameter of the chest wall during breathing function by means of two accelerometer sensors worn on subject's rib cage and abdomen. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are also discussed.