Fine-Grained Activity
Recognition by Aggregating Abstract Object Usage
Proc. of IEEE 9th International Symposium on
Wearable Computers (ISWC) 2005.
Abstract
In this paper we present results related to achieving finegrained
activity recognition for context-aware computing
applications. We examine the advantages and challenges
of reasoning with globally unique object instances detected
by an RFID glove. We present a sequence of increasingly
powerful probabilistic graphical models for activity recognition.
We show the advantages of adding additional complexity
and conclude with a model that can reason tractably
about aggregated object instances and gracefully generalizes
from object instances to their classes by using abstraction
smoothing. We apply these models to data collected
from a morning household routine.