Common Sense Based Joint Training of Human Activity Recognizers
Shiaokai Wang, William R. Pentney, Ana-Maria Popescu, Tanzeem Choudhury, Matthai Philipose · 2007
Given sensors to detect object use, commonsense priors of object usage in activities can reduce the need for labeled data in learning activity models. It is often useful, however, to understand how an object is being used, i.e., the action performed on it. We show how to add personal sensor data (e.g., accelerometers) to obtain this detail, with little labeling and feature selection overhead. By synchronizing the personal sensor data with object-use data, it is possible to use easily specified commonsense models to minimize labeling overhead.