A Method for Activity Recognition Partially Resilient on Mobile Device Orientation

This paper demonstrates a method for activity recognition partially resilient on mobile device orientation, by using data from a mobile phone embedded accelerometer. This method is partially resilient on mobile device orientation, in such a way that a mobile device can be rotated around only one axis for an arbitrary angle. The classifier for activity recognition is built using data from one default orientation. This method introduces a calibration phase in which the phone’s orientation is determined. After that, accelerometer data is transformed into the default coordinate system and further processed. The solution is compared with the method that built a classifier using data from multiple orientations. Three classifiers were tested and a high accuracy of around 90% was achieved for all of them.