Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes

Casagrande, Flàvia Dias; Tørresen, Jim; Zouganeli, Evi

doi:10.1007/978-3-030-30490-4_37

Cited by 2 publications

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References 16 publications

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“…The best-performing algorithm is further improved by including the time of occurrence information in several ways. Part of this work has been previously published [10]- [13], however, using data from one apartment only. We have also examined the prediction accuracy across some of the apartments and the performance when using transfer learning [14].…”

Section: Introductionmentioning

confidence: 99%

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

2019

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We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each-including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment. INDEX TERMS Binary sensor, probabilistic method, recurrent neural network, sequence and time prediction, transfer learning.

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Section: Introductionmentioning

confidence: 99%

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

2019

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Prediction of Next Sensor Event and its Time of Occurrence using Transfer Learning across Homes

Casagrande

Zouganeli

2019

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)

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This thesis is written for the Faculty of Mathematics and Natural Sciences at the University of Oslo for the degree of Philosophiae Doctor (Ph.D.). The research presented here has been conducted under the supervision of Associate Professor Evi Zouganeli and the co-supervision of Professor Jim Tørresen. The work has been funded by the Research Council of Norway, under the SAMANSVAR programme (247620/O70) and is a part of the interdisciplinary "Assisted Living Projectresponsible innovations for dignified lives at home for people with mild cognitive impairment or dementia". The project involves researchers from the

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Prediction of the Next Sensor Event and Its Time of Occurrence in Smart Homes

Cited by 2 publications

References 16 publications

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Prediction of Next Sensor Event and its Time of Occurrence using Transfer Learning across Homes

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