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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.

Occupancy and Daily Activity Event Modelling in Smart Homes for Older Adults with Mild Cognitive Impairment or Dementia