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

Action2Activity: Recognizing Complex Activities from Sensor Data