The wide availability of mobile devices and wireless connectivity have opened the way to the emergence of IoT technology. IoT is a network of objects that act as active participants in different contexts. This sophisticated network produce a valuable sources of information, that marged with social media has the potential to improve healthcare. Social media play a huge role in a lot of people's lives since more and more people are sharing information on social networking services creating a communicative and collaborative connection. This also happens in relation to health and well-being. The synergy between the IoT healthcare and social media data is promising to monitor state of health of patients. This paper proposes an IoT architecture that allows to collect and analyze huge volumes of heterogeneous clinical data. A central point is a methodology for extracting and mining health-related data from social media. The harvested health knowledge together with data from IoT devices may be explored through machine learning techniques for outbreak detection and analysis of spreading dynamics in a geographic area.
Integrating IoT and social media for smart health monitoring
Comito C;Falcone D;Forestiero A
2020
Abstract
The wide availability of mobile devices and wireless connectivity have opened the way to the emergence of IoT technology. IoT is a network of objects that act as active participants in different contexts. This sophisticated network produce a valuable sources of information, that marged with social media has the potential to improve healthcare. Social media play a huge role in a lot of people's lives since more and more people are sharing information on social networking services creating a communicative and collaborative connection. This also happens in relation to health and well-being. The synergy between the IoT healthcare and social media data is promising to monitor state of health of patients. This paper proposes an IoT architecture that allows to collect and analyze huge volumes of heterogeneous clinical data. A central point is a methodology for extracting and mining health-related data from social media. The harvested health knowledge together with data from IoT devices may be explored through machine learning techniques for outbreak detection and analysis of spreading dynamics in a geographic area.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.