This project will create a new community information infrastructure resource that consists of datasets of dense, multi-modal sensor records of activity of volunteers who live in an instrumented home environment. Researchers in computer science, engineering, user-interface design, and telemedicine fields are currently developing technologies for the home that exploit new sensing capabilities to create context-aware environments that can proactively deliver information to occupants wherever and whenever it is appropriate, based on the occupants' activities. Context is detected using inference algorithms that process data from sensors distributed throughout the environment, worn on the body, or both. This community resource will include high-quality, synchronized data streams from most of the sensor types currently being used in activity research (object usage, RFID, wearable accelerometers, video, audio, etc.). It will allow researchers to focus on developing and testing in-home techniques for context-detection, without being stalled by the steep requirements of data collection.
With the resource to be created in this project, researchers will be able to quickly assess the viability of inference algorithms they propose on datasets that capture realistic home behavior, and compare algorithms across different behaviors of interest and sensor modalities. The datasets created through this work will allow researchers to determine the minimal set of sensors required to reliably detect specific activities, thereby making larger scale testing of prototypes in other homes more practical. Rather than requiring months of work to collect data and run a test of an algorithm on data from a home, researchers will be able to quickly try different approaches using the community resource.
This infrastructure will accelerate research on use of information technology to create novel applications in the home, especially those designed to help people stay healthy as they age through medical support and monitoring in the home. The datasets will assist researchers creating context-sensitive user interfaces that are customized to particular individuals and automatically present information at times when it is needed. This work will have application in the fields of user interface design, preventive healthcare, and ubiquitous computing. Researchers studying communication, education, health, work practices, entertainment systems, and energy conservation may all be able to learn how to create easy-to-use and meaningful home technologies from analysis of the datasets this information infrastructure will offer. The freely-available multi-modal datasets of home activity can be used by graduate and undergraduate students interested in developing advanced ubiquitous computing technology for the home.
