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Several types of sensors have been available in off-the-shelf mobile devices,including motion, magnetic, vision, acoustic, and location sensors. This paperfocuses on the fusion of the data acquired from motion and magnetic sensors,i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition ofActivities of Daily Living (ADL) using pattern recognition techniques. Thesystem developed in this study includes data acquisition, data processing, datafusion, and artificial intelligence methods. Artificial Neural Networks (ANN)are included in artificial intelligence methods, which are used in this studyfor the recognition of ADL. The purpose of this study is the creation of a newmethod using ANN for the identification of ADL, comparing three types of ANN,in order to achieve results with a reliable accuracy. The best accuracy wasobtained with Deep Learning, which, after the application of the L2regularization and normalization techniques on the sensors data, reports anaccuracy of 89.51%.

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living