Tham khảo tài liệu 'smart home systems part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 126 Smart Home Systems 4. Persistence modelling approaches The easiest way to analyse location data and extract from them preliminary trends in daily routine consists in the establishment of presence curves Fig 3. Virone 2009 . To go on further more sophisticated random processes techniques may be used. Classical time series techniques like Box-Jenkins auto-regressive processes have already been tested for modelling location succession Das and Roy 2008 Virone et al. 2002 2003 a b c . Among the various possible approaches for modelling the actimetric data two methods have been selected. The first one is based on a generalized Polya s urns scheme Fouquet et al. 2009 b in which the observed activity at time t depends on the whole past since a reset supposed to be made at the beginning of each day . The second one concerns a first order Markov chain approach in which the dependency of the future of t 1 lies only through the present time t. In both models a persistence parameter is defined. To decide which method suits the most a criteria based on the empirical mean Ei of remaining duration in a task i was proposed. Circadian Activity Rhythms CAR Fig. 3. Presence curves in different flat rooms Polya s urns model Polya s urns were used in 1923 by Eggenberger and Pólya to model the spread of contagious diseases. Since then they were applied to various domains like climatology for the sequence of dry and wet days Galloy et al. 1983 . The success of the Polya s urns scheme may be accounted by the fact that it is a visual mechanism easy to interpret in contrast with some abstract principles of probabilities Kotz et al. 2000 Inoue and Aki 2001 . Regarding the scheme a generalized Polya s urn is an urn containing initially bo balls of N different colours split as follow a0 i balls of colour i for i from 1 to N with b0 EịLiao i . 127 Telemonitoring of the elderly at home Real-time pervasive follow-up of daily routine automatic detection of outliers and drifts Bedroom Living
