  As compared to simple actions, activities are much more complex, but
semantically consistent with a human's real life. Techniques for action
recognition from sensor generated data are mature. However, there has been
relatively little work on bridging the gap between actions and activities. To
this end, this paper presents a novel approach for complex activity recognition
comprising of two components. The first component is temporal pattern mining,
which provides a mid-level feature representation for activities, encodes
temporal relatedness among actions, and captures the intrinsic properties of
activities. The second component is adaptive Multi-Task Learning, which
captures relatedness among activities and selects discriminant features.
Extensive experiments on a real-world dataset demonstrate the effectiveness of
our work.
