Human Activity Recognition using Triaxial Acceleration Data from Smartphone and Ensemble Learning

In recent years, the use of smartphone sensors in human activity recognition (HAR) has been well studied. Mostly, a smartphone accelerometer has played the main role to solve the problem of HAR. However, the role of a gyroscope is to be explored, both when used alone as well as in combination with an accelerometer. For this purpose, the researchers investigated the role of these two smartphone sensors in human activity recognition. Two ensemble learning based approaches, i.e., majority voting and stacking, to improve recognition performance were presented. Also, the researchers evaluated the roles of the approaches on two body positions using the two ensemble classifiers while recognizing six physical activities, i.e., standing, sitting, laying, walking, walking upstairs, and walking downstairs. From the experimental results, it was shown that in general an accelerometer and a gyroscope complement each other, thereby making the recognition performance higher. Moreover, the ensemble learning based approaches could improve the recognition performance in terms of accuracy to 91.1667 percent.