Projects per year
Predicting human behaviour from smart-house data is an active and challenging area of research. Applications include improving quality of life and personal healthcare, e.g. smart thermostats and detecting falls. Smart-house technology is becoming increasingly popular and in many cases making a house smarter is as simple as installing off-the-shelf devices. In this paper we focus on predicting the future location of a person in a simulated smart-house environment. We consider three different occupant types who exhibit various working patterns. We develop a versatile model capable of adjusting to significant changes in a person's behaviour without the need of model retraining. To this end, we build a simple event calculus framework based on the Aleph Inductive Logic Programming system. Event calculus helps to handle time and persisting sensor states. Background knowledge encodes important information about the smart-house that is otherwise difficult to learn; it also facilitates transferability of the model to different house layouts. Moreover, rule models are white-box, hence human-readable. Finally, we show that a versatile model performs significantly better than other models that do not explicitly account for the context.
|Number of pages||7|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 22 Jun 2016|
- Digital Health
- Event calculus
- Location prediction
- Versatile model
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent