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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.

In this paper we present event anticipation and prediction of sensor data in a smart home environment with a limited number of sensors. Data is collected from a real home with one resident. We apply two state-of-the-art Markovbased prediction algorithms − Active LeZi and SPEED − and analyse their performance with respect to a number of parameters, including the size of the training and testing set, the size of the prediction window, and the number of sensors. The model is built based on a training dataset and subsequently tested on a separate test dataset. An accuracy of 75% is achieved when using SPEED while 53% is achieved when using Active LeZi.

We present preliminary results on sensor data prediction in a smart home environment with a limited number of binary sensors. The data has been collected from a real home with one resident over a period of 17 weeks. We apply Recurrent Neural Network with Long Short-Term Memory to a text sequence derived from the sensors' events to predict the next event in a sequence. We compare our system's characteristics and results to a baseline method and to similar work in the area. Our implementation achieved a peak accuracy of 69% for a set with 13 sensors in total-motion, magnetic and power sensors-and 75% for five motion sensors.