document: 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 low-level 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 sensors allow us to formulate activity models by translating labeled activities , such as ` cooking pasta ' , into probabilistic collections of object terms , such as ` pot ' . Given this view of activity models as text translations , we show how to mine definitions of activities in an unsupervised manner from the web . We have used our technique to mine definitions for over 20,000 activities . We experimentally validate our approach using data gathered from actual human activity as well as simulated data .
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