Abstract
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.
Highlights
MEMS (Micro-Electro-Mechanical Systems) are small, light and cheap sensors, whose spread is continuously growing in several sectors [1]
Here we focus on comparing the performances of a base classifier with a hierarchical one that combines two base classifiers: a device pose classifier and a walker identity classifier
MACHINE LEARNING ALGORITHMS Machine Learning (ML) is a subset of the artificial intelligence discipline aiming at realizing systems that can learn how to behave from data [11]
Summary
MEMS (Micro-Electro-Mechanical Systems) are small, light and cheap sensors, whose spread is continuously growing in several sectors [1]. Supervised learning can be effectively adopted in classification problems to identify the class labels for new observations In this case, the learning process consists of selecting, among all the possible functions, the best one capable to identify correct class labels for unseen input data (i.e., data not included in the training set). High-complexity learned models are said to ‘‘over-fit’’ since they are too much linked to the specific instances of training samples. Such models are not able to generalize, often performing well on the training/test data but exhibiting bad performances on new samples that are never used in the learning process. This section explores the supervised learning algorithms since the proposed approach is based on this kind of classifier
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