© Springer International Publishing AG 2018. Navigation is an essential feature for smartphones, even indoors. Having a robust step count algorithm is the cornerstone for building an inertial navigator based on accelerometer sensors. However, accelerometer data is very sensitive to body movements, so separating noise from real steps is not a trivial issue. Our main hypothesis is that Mean Squared Error (MSE) measured between predicted and real signal gives a clear distinction between ideal steps, noisy steps and pure noise. In this paper we propose a combination of techniques to obtain a robust step count model for smartphones. Using the vertical component of the acceleration, Support Vector Regression (SVR) for modeling user’s activity and an algorithm that combines peak-valley detection with high MSE steps filtering, we achieve a computational efficient and robust model for detecting steps.
Keywords: Inertial navigation, Step detector, SVR modeling, Walk recognition