Projects per year
To achieve this, the system has to reason about the person's actions and goals.
To address this challenge, we present a behaviour recognition approach that relies on symbolic behaviour representation and probabilistic reasoning to recognise the person's actions, the type of meal being prepared and its potential impact on a patient's health.
We test our approach on a cooking dataset containing unscripted kitchen activities recorded with various sensors in a real kitchen.
The results show that the approach is able to recognise the sequence of executed actions and the prepared meal, to determine whether it is healthy, and to reason about the possibility of depression based on the type of meal.
|Title of host publication||IEEE International Conference on Pervasive Computing and Communications|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 2017|
- Digital Health
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