Daily Activity Model for Ambient Assisted Living

We propose a novel way for ambient assisted living: a system that with motion detector to observe the daily activities of the elderly, build the daily activity model of the user. In case of unusual activities the system send alarm signal to caregiver. The problems with this approach to build such a model: firstly, the activities of the user are random and dynamic distributed, that means the related data is dynamically and with huge count. Secondly, the difficulty and computational burden to get character parameters of hidden Markov model with many "states". To deal with the first problem we take advantage of an easy filter algorithm and translate the huge dynamical data to "state" data. Secondly according the limited output of distinct observation symbols per state, we reduced the work to research the observation symbol probability distribution. Furthermore the forward algorithm used to calculate the probability of observed sequence according the build model.