Abstract
The problem of activity recognition is a topic that has been explored in the field of ubiquitous computing, the popularization of sensors on the most diverse types has been instrumental in improving the effectiveness of recognition. Smartphones offer a range of sensors (GPS, Accelerometer, Gyroscopes, etc.) that can be used to provide data for this type of problem. This work proposes a new model of activity recognition in GPS captured data and enriched with POIs (Points Of Interest) and user profile. The experiment was performed by 10 volunteers collecting data for 10 days. The model aims to recognize 13 different activities, divided into stop activities (Bank, breakfast, dining, lunch, praying, recreation, shopping, studying, waiting transport and working) and moves activities (in a car, on a bus and walking). The model was tested and compared to J48, SVM, ANN and RF algorithms, and obtained 97.4% hits.
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da Penha Natal, I., de Avellar Campos Cordeiro, R., Garcia, A.C.B. (2017). Activity Recognition Model Based on GPS Data, Points of Interest and User Profile. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_35
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DOI: https://doi.org/10.1007/978-3-319-60438-1_35
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