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Human Dynamics Group


Incorporating new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems world-wide become strained by aging populations. The LiveNet platform is a flexible distributed mobile system that can be deployed for a variety of proactive health applications that can help reduce the strain on the healthcare industry. LiveNet enables a variety of large-scale wireless group applications by leveraging the availability of Linux-based PDA hardware combined with innovative open-source software and custom sensor hardware. The LiveNet system is based on the MIThril 2003 architecture, a proven accessible architecture that combines inexpensive commodity hardware, a flexible sensor/peripheral interconnection bus, and a powerful light-weight distributed sensing, classification, and inter-process communications software layer to facilitate the development of distributed real-time multimodal and context-aware applications. LiveNet also opens up the door for practical long-term continuous monitoring applications to identify physiological and behavioral trends that vary slowly with time. The system can also allow people to receive real-time feedback from their continuously monitored and analyzed health state, as well as communicate health information with care-givers and other members of an individual's social network for support and interaction.


GroupMedia is a set of tools and applications to enable social context awareness and quantitative intelligence on pervasive cell phones and PDAs. By building quantitative models of human behavior and social interaction, we can devise next generation social software for the wearable devices.

Reality Mining

The Reality Mining experiment is one of the largest academic mobile phone projects in the US. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.

Learning Humans

The purpose of this set of projects is to develop techniques for learning human behavior in an office or a social situation. Projects include learning both human control and interactive behaviors. We want to build machines that understand person's intentions by the set of subtle queues and patterns of his regular behavior. This will help to seamlessly integrate computers into our everyday lives.