Identification of Walker Identity Using Smartphone Sensors: An Experiment Using Ensemble Learning

Nowadays MEMS sensors, like accelerometers, gyroscopes, and magnetometers, are spreading in a wide range of applications, because of their small size, cheapness and increasing performance. For instance, smartphones are currently equipped with this kind of sensors, which could be used to improve the user experience of the phone itself or the navigation functionalities. In this work, accelerometers, gyros, and orientation measurements are exploited to provide advanced information about the walker bringing the phone. In particular, smartphone sensors outputs are used to recognize the identity of the walker and the pose of the device during the walk. The aforesaid information, if known, could be used to improve specific smartphone functionalities. For instance, the recognition of walker identity can be used for theft protection or the device pose can be used to improve the performance of the pedestrian navigation. Machine learning algorithms have been effectively adopted in several fields to solve problems involving classification, time series prediction, pattern recognition, and object detection. Herein, a novel hierarchical approach for classification is applied to data produced by smartphone sensors in order to recognize the previously described contexts, obtaining effective results.