A wealth of data that can be useful to health monitoring is often available in Social Networking Sites, such as Facebook. This includes a person’s social data (friends, connections, group memberships) which is typically hard to obtain by traditional means, as well as user-generated content (Facebook wall posts, tweets). Due to their unstructured nature, gleaning meaning from this user-generated content typically requires text mining or natural language processing techniques which need specialized vocabularies and may prove inaccurate. We propose using Social Network applications, such as those provided by the Facebook Developer platform to get the best of both worlds – structured user-submitted content as well as social data and other useful information present on the Social Networking platform. We employ Semantic Web techniques to convert an application’s output to sensor observations in order to a) homogenize and integrate data from multiple applications and b) treat applications as Social Sensors, efficiently integrating physical and social sensors where needed. With the appropriate application design, a social network that is application-capable can become an infinite repository of human sensor observations and their accompanying social data that can be queried and used for a variety of feature-rich health applications. We present the design of our prototype monitoring framework, SENHANCE, which includes a Facebook app and show how it can used for health monitoring.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- Using Social Network Apps as Social Sensors for Health Monitoring