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.
La Concepcion et al. REF leverage data retrieved from accelerometer sensors to generate discrete variables, then the core of the algorithm Ameva is used to develop the selection, discretization, and classification technique for activity recognition.
Mobile activity recognition and fall detection system for elderly people using Ameva algorithm
Pervasive Mob. Comput.
Pervasive Mob. Comput.
Computer Science