Activity models play a critical role for activity recognition and assistance in ambient assisted living. Existing approaches to activity modeling suffer from a number of problems, e.g., model reusability, and incompleteness. In an effort to address these problems, we introduce an ontology-based hybrid approach to activity modeling that combines domain knowledge based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are deployed, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this paper focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. The approach has been implemented in a feature rich assistive living system. Analysis of the experiments conducted has been undertaken in an effort to test and evaluate the activity learning algorithms and associated mechanisms. An Ontology-Based Hybrid Approach to Activity Modeling for Smart Homes
The Existing system is data driven approach, the drawback of this system is related to model applicability and reusability. A data-driven approach is sensitive to unseen data which makes it difficult to apply the ADL models which have been learnt from one person to another person. This means that with a data-driven approach, an activity model for different users has to be learnt separately. The third drawback is the incompleteness of activity models which is closely related to the aforementioned two issues. With the data-driven approach, every activity model for all the ADLs for every user needs to be learnt in order to create complete ADL models. Given the large number of ADLs this is a huge challenge, if indeed not impossible, in practice. To mitigate the aforementioned problems, researchers have recently started applying transfer learning techniques to activity modeling and recognition by reusing resources and knowledge. This involves transferring the source datasets, or features or models, from one user to another in different settings. An alternative to the data-driven approach is to manually define activity models by making use of rich, prior knowledge, and domain heuristics.
This approach is motivated by the observation that most ADLs are daily routines which normally take place within a specific circumstance of time, location, and space with relatively fixed types of objects. Using formal knowledge acquisition and modeling technologies, activity models can be created by means of various knowledge modeling tools. As this approach is closely related to knowledge engineering, it is referred to as a knowledge driven approach. A knowledge-driven approach improves model reusability by modeling activities at multiple levels of abstraction to create both generalized and specialized ADL models. For example, ontological activity modeling can model a generic ADL as an ontological activity class and an individual-specific ADL as an instance of the corresponding activity class. ADL modeling is not a one-off effort, instead, a multiphase iterative process that interleaves knowledge-based model specifications and data-driven model learning. The process consists of two key phases.
Ontological activity modeling creates activity models at two levels of abstractions, namely as ontological activity concepts and their instances, respectively. Ontological activity concepts represent generic coarse-grained activity models applicable and reusable for all users, thus solving the reusability problem. The seed ADL models are then used in applications for activity recognition at the second phase. In the third phase, the activity classification results from the second phase are analyzed to discover new activities and user profiles. These learnt activity patterns are in turn used to update the ADL models, thus solving the incompleteness problem. Once the first phase completes, the remaining two-phase process can be iterated many times to incrementally evolve the ADL models, leading to complete, accurate, and up-to-date ADL models. This paper makes three main contributions. First, we develop a hybrid approach to activity modeling that combines the strengths of data- and knowledge-driven approaches to support an incremental modeling process. The approach is built upon the work in however, extends it by incorporating the learning capabilities to provide a viable solution for addressing existing problems relating to ADL modeling. Second, we develop a learning method to discover activities that are performed by users but have not yet been modeled. Third, we define the characteristics of a user activity profile and develop analysis methods and associated inference rules to learn a user’s activity profiles, i.e., the specific way the user performs activities. The learning methods of activity profiles can detect the changing manner an activity is performed, thus allowing ADL models to adapt over time. We have implemented the approach in a feature-rich assistive living system.