Human Activity Recognition through Weighted Finite Automata

Abstract

ABSTRACT: This work addresses the problem of human activity identification in an ubiquitous environment, where data is collected from a wide variety of sources. In our approach, after filtering noisy sensor entries, we learn user?s behavioral patterns and activities? sensor patterns through the construction of weighted finite automata and regular expressions respectively, and infer the inhabitant?s position for each activity through frequency distribution of floor sensor data. Finally, we analyze the prediction results of this strategy, which obtains 90.65% accuracy for the test data.+