Abstract
We present work on sequential sensor events in smart homes with results on the prediction of the next sensor event and its time of occurrence in the same model using Recurrent Neural Network with Long Short-Term Memory. We implement four configurations for converting binary sensor events and elapsed time between events into different input sequences. Our dataset has been collected from a real home with one resident over a period of 40 weeks and contains data from a set of fifteen sensors including motion, magnetic, and power sensors. When including the time information in the input data, the accuracy of predicting the next sensor event was 84%. In our best implementation, the model is able to predict both the next sensor event and the mean elapsed time to the next event with a peak average accuracy of 80%.
F. D. Casagrande—Financed by the Norwegian Research Council under the SAMANSVAR programme (247620/O70).
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Acknowledgement
The authors would like to thank the residents and the housekeepers at the seniors’ care unit Skøyen Omsorg+; Torhild Holthe and Dr. Anne Lund (OsloMet) for recruiting participants for the trial and communicating with the residents throughout the trial; Dejan Krunić and Øyvind Width (Sensio AS) for installations of the sensors; and the rest of the participants of the Assisted Living Project for a fruitful interdisciplinary collaboration.
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Casagrande, F.D., Tørresen, J., Zouganeli, E. (2019). Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_37
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