A context-aware recognition survey for data collection using ubiquitous sensors in the home

Authors:
Daniel H. Wilson;Anna C. Long;Chris Atkeson
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;University of Washington, Seattle, WA;Carnegie Mellon University, Pittsburgh, PA
Venue:
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Year:
2005

Citing 4
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Abstract

Identifying what people do in the home can both inform ubiquitous computing application design decisions and provide training data to the machine learning algorithms used in their implementation. This paper describes an unsupervised technique in which contextual information gathered by ubiquitous sensors is used to help users label a multitude of anonymous activity episodes. This context-aware recognition survey is a game-like computer program in which users attempt to correctly guess which activity is happening after seeing a series of symbolic images that represent sensor values generated during the activity. We report a user study of the system, focusing on how well subjects were able to recognize their own activities, the activities of others, and counterfeits that did not correspond to any activity.