Engineering Applications Of Artificial Intelligence, 2018; 67: 136-156.
Tipo di articolo: Journal Article,
Impact factor: 2.819, Impact factor a 5 anni: 0
Url: Non disponibile.
Parole chiave: Heart Rate, Spo2, Pedometer, Health Monitoring,
*** IBB - CNR *** National Research Council of Italy — Institute for High Performance Computing and Networking (ICAR), Via P. Castellino 111, 80131 Naples, Italy; Neatec S.p.A., Via Campi Flegrei, 34, 80078 Pozzuoli, Italy
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The last decade has witnessed an exponential increase in older adult population suffering from chronic life-long<br>diseases and needing healthcare. This situation has highlighted a need to revolutionize healthcare and provide<br>innovative, efficient, and affordable solutions to patients at any time and from anywhere in an economic and<br>friendly manner. The recent developments in sensing, mobile, and embedded devices have attracted considerable<br>attention toward mobile health monitoring applications. However, existing architectures aimed at facilitating the<br>realization of these mobile applications have shown to be not suitable to address all these challenging issues: (i)<br>the seamless integration of heterogeneous devices; (ii) the estimation of vital parameters not measurable directly<br>or measurable with a low accuracy; (iii) the extraction of context information pertaining to the patient’s activity to<br>be used for the interpretation of vital parameters; (iv) the correlation of physiological and contextual information<br>to detect suspicious anomalies and supply alerts; (v) the notification of anomalies to doctors and caregivers only<br>when their detection is accurate and appropriate. In light of the above, this paper presents a smart mobile, selfconfiguring,<br>context-aware architecture devised to enable the rapid prototyping of personal health monitoring<br>applications for different scenarios, by exploiting commercial wearable sensors and mobile devices as well as<br>knowledge-based technologies. This architecture is organized as a composition of four tiers that operate on a<br>layered fashion and it exploits an ontology-based data model to ensure intercommunication among these tiers and<br>the monitoring applications built on the top of them. The proposed architecture has been implemented for mobile<br>devices equipped with the Android platform and evaluated with respect to its modifiability by employing the<br>ALMA (Architecture Level Modifiability Analysis) method, highlighting its capability of being rapidly customized,<br>personalized or eventually modified by software developers in order to prototype, with a reduced effort, novel<br>health monitoring applications on the top of its components. Finally, it has been employed to build, as case<br>study, a mobile application aimed at monitoring and managing cardiac arrhythmias, such as bradycardia and<br>tachycardia, confirming its effectiveness with respect to a real scenario.
37 Records (34 escludendo Abstract e Conferenze). Impact factor totale: 102.154 (100.154 escludendo Abstract e Conferenze). Impact factor a 5 anni totale: 117.005 (106.459 escludendo Abstract e Conferenze).