Abstract:
Advances in pervasive and ubiquitous computing
have resulted in the development of sensors that can be easily
deployed in the natural habitat of a human to acquire activity
related data. However, inferring meaningful activity information
from sensor data is still a challenging problem. This paper
addresses the problem of inferring activities that are
simultaneously performed by multiple residents in a smart home
or single resident performing multiple activities concurrently.
The paper formulates this problem as learning multiple activity
labels from a sequence of sensor data. It investigates the
suitability of multi-label learning algorithms inspired by decision
trees as a proposed solution to the problem. The results obtained
from the experiments on four benchmarking multi-resident
activity datasets clearly indicate the superiority of decision tree
ensemble (random forests) based approaches for multi-label
learning
