From action to activity: Sensor-based activity recognition.

As compared to actions, activities are much more complex, but semantically they are more representative of a human׳s real life. Techniques for action recognition from sensor-generated data are mature. However, few efforts have targeted sensor-based activity recognition. In this paper, we present an efficient algorithm to identify temporal patterns among actions and utilize the identified patterns to represent activities for automated recognition. Experiments on a real-world dataset demonstrated that our approach is able to recognize activities with high accuracy from temporal patterns, and that temporal patterns can be used effectively as a mid-level feature for activity representation.