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 requiredMinisterio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucía Simon P11-TIC-8052Junta de Andalucía M-Learning P11-TIC-712
