Light-Weight Online Unsupervised Posture Detection by Smartphone Accelerometer

This paper proposes a light-weight online classification method to detect smarthpone user's postural actions, such as sitting, standing, walking, and running. These actions are named as "user states" since they are inferred after the analysis of data acquired from the smartphones equipped accelerometer sensors. To differentiate one user state from another, many studies can be found in the literature. However, this study differs from all others by offering a computational lightweight and online classification method without knowing any priori information. Moreover, the proposed method not only provides a standalone solution in differentiation of user states, but also it assists other widely used offline supervised classification methods by automatically generating training data classes and/or input system matrices. Furthermore, we improve these existing methods for the purpose of online processing by reducing the required computational burden. Extensive experimental results show that the proposed method makes a solid differentiation in user states even when the sensor is being operated under slower sampling frequencies.