Mining models of human activities from the web

The ability to determine what day-to-day activity (such as cooking pasta, taking a pill, or watching a video) a person is performing is of interest in many application domains. A system that can do this requires models of the activities of interest, but model construction does not scale well: humans must specify lowlevel details, such as segmentation and feature selection of sensor data, and high-level structure, such as spatio-temporal relations between states of the model, for each and every activity. As a result, previous practical activity recognition systems have been content to model a tiny fraction of the thousands of human activities that are potentially useful to detect. In this paper, we present an approach to sensing and modeling activities that scales to a much larger class of activities than before. We show how a new class of sensors, based on Radio Frequency Identification (RFID) tags, can directly yield semantic terms that describe the state of the physical world. These...

• Type: Conference
• Year: 2004
• Where: WWW
• Authors: Mike Perkowitz, Matthai Philipose, Kenneth P. Fishkin, Donald J. Patterson
• Keywords: Activity Models, Actual Human Activity, Internet Technology, Keywords Activity Inference, WWW 2004