Activity Recognition for Personal Time Management

Authors:
Zoltán Prekopcsák;Sugárka Soha;Tamás Henk;Csaba Gáspár-Papanek
Affiliations:
Budapest University of Technology and Economics, Hungary;Budapest University of Technology and Economics, Hungary;Budapest University of Technology and Economics, Hungary;Budapest University of Technology and Economics, Hungary
Venue:
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Year:
2009

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Abstract

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.