Activity Recognition Based on the Dynamic Coordinate Transformation of Inertial Sensor Data

The orientation of a mobile phone is a fundamental challenge in activity recognition (AR) on this device that significantly affects recognition accuracy. In this study, we propose a novel orientation-independent method to eliminate the influence of orientation changes on AR. This method is a dynamic coordinate transformation approach on inertial sensor data. In this method, the data collected in different orientations are dynamically mapped to the reference coordinate system of a mobile phone. The classification on the mapped data can reach much higher accuracy than on the original data. We evaluated our method using four sets of comparative experiments. The proposed method was compared with initial processes without considering orientation, two other methods in previous studies. The experiments showed that the proposed method outperformed the other three approaches. Implementing a real-time activity recognition system on an Android platform demonstrated that the proposed method obtained valid recognition results despite various orientation changes.