Comparison of Feature Classification Algorithm for Activity Recognition Based on Accelerometer and Heart Rate Data

Abstract

This paper describes a project to compare two feature classification algorithms used in activity recognition in relation to accelerometer and heart rate data. Data was collected from six male and female subjects using a single tri-axial accelerometer and heart monitor attached to each subject’s dominant thigh. Subjects carried out eight activities and the data was labelled semi-automatically. Features (mean, standard deviation, energy, correlation and mean heart rate) were extracted from the data using a window of 256 (3.4 seconds) and an overlap of 50%. Two classifers, k-NN and J48, were evaluated for activity recognition with 10-fold validation with k-NN (k = 1) achieving a better overall score of 90.07%