Efficient out-of-home activity recognition by complementing GPS data with semantic information
Keywords:Enriched Sensor Data, Human Activity Recognition, Semantic Information, User Profile, Machine Learning
Smartphones have become an indispensable human device due to their increasing functionalities and decreasing prices. Their embedded sensors, including global positioning system (GPS), have opened opportunities to support human activity recognition, both indoor (in assisted living, for instance) and outdoor. This paper proposes a minimalist activity recognition model for out-of-home environments based on a smartphone. The only sensor used is GPS, whose data is enriched with semantic knowledge extracted online from the Internet, and with brief user’s profile data collected off-line. We conducted an experiment for 20 days with 22 subjects in their day to day life, with identification of 13 selected activities, of which three were performed in movement. Experimental results show that the approach has a high activity recognition performance. This demonstrates that an adequate combination of information with different levels of semantic content can produce an efficient non-invasive solution to monitoring human activity in out-of-home environments.
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