In this paper, a system based on the generation of a Hidden Markov Model from the past GPS log and cur- rent location is presented to predict a user’s destination when beginning a new trip. This approach dras- tically reduces the number of points supplied by the GPS device and it permits a ‘‘support-map” to be generated in which the main characteristics of the trips for each user are taken into account. Hence, in contrast with other similar approaches, total independence from a street-map database is achievedMinisterio de Educación y Ciencia TSI2006–13390-C02–02Junta de Andalucia TIC214

Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.