Abstract: Mobile devices are becoming increasingly sophisticated. These devices are inherently sensors for collection and communication of textual and voice signals. In a broader sense, the latest generation of smart cell phones incorporates many diverse and powerful sensors such as GPS (Global Positioning Systems) sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. So, it is not surprising that modern mobile devices, particularly cell phones of last generations that work on different mobile operating systems, got equipped with quite sensitive sensors. This paper is devoted to one approach that solves human activity classification problem with help of a mobile device carried by user. Current method is based on K-Nearest? Neighbor algorithm (K-NN). Using the magnitude of the accelerometer data and K-NN algorithm we could identify general activities performed by user.

Keywords: human activity classification; K-NN algorithm; mobile devices; accelerometer; Android platform

Link:

ACTIVITY RECOGNITION USING K-NEAREST NEIGHBOR ALGORITHM ON SMARTPHONE WITH TRI-AXIAL ACCELEROMETER

Sahak Kaghyan, Hakob Sarukhanyan

http://www.foibg.com/ijima/vol01/ijima01-2-p06.pdf