Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification

Abstract

This paper presents details of a convenient and unobtrusive system for monitoring daily activities. A smart phone equipped with an embedded 3D-accelerometer was worn on the belt for the purposes of data recording. Once collected the data was processed to identify 6 activities offline (walking, posture transition, gentle motion, standing, sitting and lying). The processing technique adopted a novel hierarchical classification. In the first instance, rule-based reasoning is used to discriminate between motion and motionless activities. Following this the classification process utilizes two multiclass SVM (support vector machines) classifiers to classify the motion and motionless activities, respectively. The classifiers were trained on data from one subject and tested on 10 subjects. The experiments demonstrate that the hierarchical method can reduce misclassification between motion and motionless activities. The average accuracy was improved compared with using a single classifier by using this classification method (82.8% vs. 63.8%), and is important for providing appropriate feedback in free living applications.