title: SENHANCE: A Semantic Web framework for integrating social and hardware sensors in e-Health.
abstract: Self-reported data are very important in Healthcare, especially when combined with data from sensors. Social Networking Sites, such as Facebook, are a promising source of not only self-reported data but also social data, which are otherwise difficult to obtain. Due to their unstructured nature, providing information that is meaningful to health professionals from this source is a daunting task. To this end, we employ Social Network Applications as Social Sensors that gather structured data and use Semantic Web technologies to fuse them with hardware sensor data, effectively integrating both sources. We show that this combination of social and hardware sensor observations creates a novel space that can be used for a variety of feature-rich e-Health applications. We present the design of our prototype framework, SENHANCE, and our findings from its pilot application in the NutriHeAl project, where a Facebook app is integrated with Fitbit digital pedometers for Lifestyle monitoring.
authors.affiliation: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece ipagkalo@auth.gr.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Pagkalos
authors.affiliation: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece.
authors.forename: Loukas
authors.initials: L
authors.lastname: Petrou
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D015438:Health Behavior / Q000502:physiology*; D000070497:Healthy Lifestyle; D006801:Humans; D057566:Self Report; D060756:Social Networking; D018511:Systems Integration; D017216:Telemedicine / Q000379:methods*
keywords: Semantic Web; e-Health; lifestyle monitoring; social networks; social sensor