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Human Activity Recognition through Weighted Finite Automata

Axpe Consulting Cantabria S.L., 39600 Camargo, Cantabria, Spain
Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, 39005 Santander, Cantabria, Spain
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Presented at the 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018.‬‬‬‬‬‬‬‬‬‬‬
Proceedings 2018, 2(19), 1263;
Published: 25 October 2018
(This article belongs to the Proceedings of UCAmI 2018)
PDF [346 KB, uploaded 25 October 2018]


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.
Keywords: human activity recognition; weighted finite automaton; regular expression; pattern mining human activity recognition; weighted finite automaton; regular expression; pattern mining
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Salomón, S.; Tîrnăucă, C. Human Activity Recognition through Weighted Finite Automata. Proceedings 2018, 2, 1263.

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