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Most mobile devices include motion, magnetic, acoustic, and location sensors.They allow the implementation of a framework for the recognition of Activitiesof Daily Living (ADL) and its environments, composed by the acquisition,processing, fusion, and classification of data. This study compares differentimplementations of artificial neural networks, concluding that the obtainedresults were 85.89% and 100% for the recognition of standard ADL. Additionally,for the identification of standing activities with Deep Neural Networks (DNN)respectively, and 86.50% for the identification of the environments withFeedforward Neural Networks. Numerical results illustrate that the proposedframework can achieve robust performance from the data fusion of off-the-shelfmobile devices.

A Multiple Data Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data