SENHANCE: A Semantic Web framework for integrating social and hardware sensors in e-Health

Area of Science:

• Digital Health
• Health Informatics
• Social Computing

Background:

• Self-reported data and sensor data are crucial in healthcare.
• Social Networking Sites (SNS) offer rich, yet unstructured, health-related data.
• Integrating diverse data sources presents significant challenges for health professionals.

Purpose of the Study:

• To develop a framework for integrating social media data with hardware sensor data.
• To leverage Social Network Applications as Social Sensors for structured data collection.
• To create a novel data space for feature-rich e-Health applications.

Main Methods:

• Employing Social Network Applications as Social Sensors.
• Utilizing Semantic Web technologies for data fusion.
• Integrating a Facebook application with Fitbit digital pedometers for lifestyle monitoring within the SENHANCE framework.

Main Results:

• Demonstrated the feasibility of combining social and hardware sensor data.
• Created a novel integrated data space for e-Health.
• Successfully piloted the SENHANCE framework in the NutriHeAl project for lifestyle monitoring.

Conclusions:

• The integration of social and hardware sensor data offers a powerful approach for e-Health.
• The SENHANCE framework enables comprehensive lifestyle monitoring through data fusion.
• This methodology supports the development of innovative, feature-rich e-Health applications.