Activity recognition has a vital role in smart home operations. One of the major challenges in object-sensor-based activity recognition is to learn the complete activity model derived from a generic activity model for sequential and parallel activities. Such challenge exists due to erratic degrees of dissimilar activities in which inhabitants perform activities in sequential and interleaved fashion while interacting with different objects. The proposed work focuses on recognizing a complete set of actions (of activity) by exploiting different knowledge engineering techniques, ontology-based temporal formalisms and data driven techniques. Semantic Segmentation has been employed to establish the generic activity model. The spurious semantic segmentation produced by sensor noise or erratic behaviour is removed by Allen’s temporal formalism. Moreover, Tversky’s feature-based similarity has been used to remove the highly similar spurious activities produced as a result of mistaken interactions with wrong home objects. The duration to perform activities varies among inhabitants; such duration intervals are identified dynamically using the proposed model in order to have a complete activity model. A comprehensive set of experiments has been carried out for evaluating the proposed model where the results based upon different metrics assert its effectiveness especially when compared with other contemporary techniques.
