Enhancing Accelerometer-based Human Activity Recognition with Relative Barometric Pressure Signal

This paper proposes the use of relative barometric pressure sensors for enhancing the accuracy of human activity detection based on a tri-axial accelerometer. In our experiment, we employ a device consisting of a tri-axial accelerometer and a barometric pressure sensor on 12 subjects' waists while they are performing different sequences of activities of sitting, standing, walking, and lying. We also set another barometric pressure sensor on the wall as a reference for barometric pressure. We compare activity classification with features extracted from the following datasets 1) acceleration data, 2) acceleration and on-body barometric pressure data, and 3) acceleration, on-body barometric pressure, and the reference barometric pressure data. Three classifiers are compared, i.e., K-Nearest Neighbors (KNN), Decision Tree, and Random Forest. The results show that highest classification accuracy can be achieved when acceleration, on-body barometric pressure, and the reference barometric pressure information are used. This dataset provides the highest classification accuracy of 90.4% with the Random Forest algorithm.