Accelerometer Signal Features and Classification Algorithms for Positioning Applications
Melania Susi, Daniele Borio, G. Lachapelle · 2011
The continuous development of Micro ElectroMechanical Sensors (MEMSs) and their integration into cell-phones and other mobile devices is pushing the design of new algorithms capable of determining the user activity. Determining what the user is doing allows one to bound his displacement and provide information about his location. The design of such algorithms is a classification problem where the different classes are specified by the MEMS location and user activity. In this paper, MEMS accelerometer signals are analyzed in different domains and several features are selected for the design of classification algorithms. Frequency domain analysis is performed as a function of the user velocity and sensor location, showing the potential of the selected features even when the MEMS is not placed on the user foot. The selected features are finally integrated into three different classification algorithms whose characteristics are analyzed and compared under several operating conditions.