  Several types of sensors have been available in off-the-shelf mobile devices,
including motion, magnetic, vision, acoustic, and location sensors. This paper
focuses on the fusion of the data acquired from motion and magnetic sensors,
i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of
Activities of Daily Living (ADL) using pattern recognition techniques. The
system developed in this study includes data acquisition, data processing, data
fusion, and artificial intelligence methods. Artificial Neural Networks (ANN)
are included in artificial intelligence methods, which are used in this study
for the recognition of ADL. The purpose of this study is the creation of a new
method using ANN for the identification of ADL, comparing three types of ANN,
in order to achieve results with a reliable accuracy. The best accuracy was
obtained with Deep Learning, which, after the application of the L2
regularization and normalization techniques on the sensors data, reports an
accuracy of 89.51%.
