LAUSR

Smartphone mode recognition is becoming a key aspect of many applications, such as daily life monitoring, health care, and indoor positioning. In the later, two approaches exist to perform positioning using smartphone inertial sensors: traditional inertial navigation algorithms and pedestrian dead reckoning (PDR). Usually, PDR is preferred since it requires less integrations on the noisy sensory data. Step length estimation is a critical stage in PDR. It requires a calibration phase to determine appropriate gains, prior to PDR application. Using an incorrect gain will result in a position error. Such gains are very sensitive to different smartphone modes, such as talking, texting, swing, or pocket. Therefore, each smartphone mode requires a different gain. In this paper, the effect of the smartphone mode on PDR positioning accuracy is highlighted. To circumvent this error source, we employ machine learning classification algorithms to recognize the smartphone modes and thereby enabling the choice of a proper gain value to improve PDR positioning accuracy. To that end, a methodology of training on a single user and testing on multiple users, as well as unique features for the classification process, is implemented. Experimental results obtained using 13 participates walking in different indoor conditions and smartphone modes, show successes of more than 95% in classifying the smartphone modes and as a consequence may improve PDR positioning performance.