User Modeling-Silicon Valley Campus - Carnegie Mellon University

User Modeling-Silicon Valley Campus - Carnegie Mellon University

User Modeling

Mobile devices are an inherently personal type of computing device. They are always-on, usually-connected, rich in their ability to sense the users context, full of user-specific infor- mation, and capable of autonomous and secure processing. We are exploring the creation of models of users behavior from observed mobile sensor time-series data. Built-in ac- celerometers, gyroscopes, magnetometers, GPS subsystems, WiFi receivers, cameras, and the microphone provide a rich source of information that can be mined in real time.


Mobile Lifelogger/ Activity Recognition

DCAP: Social and Psychological Behavior Modeling

Mobile sensors capture a users social interaction patterns from calling frequency, locations visited, and nearby devices. They also capture information such as voice, hand tremors, and typing speed that reflect a users psychological status. Using mobile sensing, we are now able to model a users social and psychological behavior, which enables the caregiver to identify psychological disorder at early stage and provides the opportunity for more effective interven- tion. The DCAP project (funded by DARPA) aims at using mobile sensing and behavior modeling to better understand and mitigate the psychological disorders of veterans suffering from PTSD and depression.
Joy Zhang

Prof. Joy Zhang

Muscle Recognition
  • Frank Mokaya, Cynthia Kuo, Quinn Jacobson, Brian Nguyen, Anthony Rowe, Pei Zhang, “MARS: A Muscle Activity Recognition System Enabling Self-configuring Musculoskeletal Sensor Networks”, 12th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN’13), 2013 (To Appear)

Virtual Medical Assistant

Currently, providing quality care for elderly people, care professionals must stay with certain patients 24 hours a day, increasing cost and infringing on privacy. To alleviate these problems, we are developing a Virtual Medical Assistant (VMA) that ubiquitously monitors the medical condition of the patient. Learn more.
Pei Zhang

Prof. Pei Zhang