Our core technology is based on cutting-edge research and science conducted at the MIT Media Lab.
The four billion mobile phones on the planet are powerful social sensors. As citizens of the information age, we leave digital traces of behavior in our communication and movement patterns.
Ginger.io uses machine learning and data mining to passively collect and analyze subtle signals of behavior change to better understand users' social, physical and mental health status.
91% of people keep their phone within 3 feet, 24 hours a day.
Morgan StanleyTechnology & Internet Trends
Real-Time Data Collection and Analysis
In order to have meaningful results, you need to have complete data. Traditional data sources provide single intermittent data points. The Ginger.io platform collects and analyzes continuous data to fill in the data gaps, providing a richer, more objective picture of how you're doing.
What's happening between data points?
Traditional data sources provide single intermittent data points that provide an incomplete view of what's happening.
See a complete picture with Ginger.io
The Ginger.io platform collects and analyzes continuous data to fill in the data gaps, providing a richer, more objective picture of how you're doing.
Data Science Meets Behavioral Science
Ginger.io taps into the continuous sensor data from your mobile phone and other devices to predict individual behavior changes and identify aggregate trends. Our research from MIT Media Lab demonstrated that location and communication sensors can be used to model individual symptoms and long term health outcomes. This research is at the core of Ginger.io's platform.
Social Sensing to Model Epidemiological Behavior Change
Proceedings of ACM Ubicomp 2010
Madan A., Cebrian M., and Pentland A.
Sensing the “Health State” of a Community
IEEE Pervasive Computing (Forthcoming)
Madan A., Cebrian M., Moturu S., Farrahi K., and Pentland A.
Social Sensing: Obesity, Unhealthy Eating and Exercise in Face-to-face Networks
Proceedings of ACM Wireless Health 2010
Madan A., Moturu S., Lazer D., and Pentland A.
Using Social Sensing to Understand the Links Between Sleep, Mood, and Sociability
Proceedings of IEEE SocialCom 2011
Moturu S., Khayal I., Aharony N., Pan W., and Pentland A.
Sleep, Mood and Sociability in a Healthy Population
Proceedings of IEEE EMBC 2011
Moturu S., Khayal I., Aharony N., Pan W., and Pentland A.
Pervasive Sensing to Model Political Opinions in Face-to-Face Networks
Pervasive Computing 2011
Madan A., Farrahi K., Gatica-Perez D., and Pentland A.