A Smart Sensing Architecture for Domestic Monitoring: Methodological Approach and Experimental Validation
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, 60131 Ancona, Italy
Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department, Università degli Studi di Genova, 16145 Genoa, Italy
Italian National Research Council, Institute for Informatics and Telematics (CNR-IIT), 56124 Pisa, Italy
Department of Information Engineering, Electronics e Telecommunications, University of Rome La Sapienza, 00184 Rome, Italy
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2310; https://doi.org/10.3390/s18072310
Received: 5 June 2018 / Revised: 13 July 2018 / Accepted: 14 July 2018 / Published: 17 July 2018
(This article belongs to the Special Issue Smart Homes)
Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident’s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user’s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user’s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.