Real Time Autonomous Point of Interest Mining through Ambient Smartphone Sensing

Abstract

The advancement of sensor equipped smartphones provides tremendous opportunities for fine-grained monitoring of user Points of Interest (POIs) for a range of mobile applications such as place-based advertisement, personalised healthcare services, location based social networks. Existing systems, however, cannot infer both indoor and outdoor POIs using a single approach and typically require a mix of technologies for localization such as GPS, GSM or Wi-Fi. The accuracy of these techniques depend on the availability of local infrastructure and normally can retrieve POIs only at a coarse level, for example at the level of a building or region. We develop a novel algorithm to automatically detect user POIs in near real time at room level accuracy using only lightweight ambient environment sensors. Our method can infer both indoor and outdoor POIs at a fine granularity without depending on local infrastructure or without using GPS or Wi-Fi. It works in an unsupervised manner using covariances of ambient sensor data to detect user visits to POIs. An experimental study with real-world data shows that our system can achieve an F1 score of approximately 80% for the top 3 retrieved locations and outperforms the existing approaches such as Google place search and Foursquare venue search.

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