Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.
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© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Activity recognition
- Anomaly detection
- Deep feature space
- Machine learning
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering