Multi-label Learning for Activity Recognition

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.