A Novelty Approach to Enhance Activity Modeling

Objectives: Cognition-driven activity recognition is a very challenging study domain. There are two main approaches to enhance activity modelings such as context knowledge and sensor dataset. Methods: The existing system used cognition- driven tool to annotate sensor activity dataset. It used Semantic Activity Annotation algorithm to annotate dataset. This produced perfect and wrong activity paradigm. It does not found frequent activity sequences. Findings: A novel technique is used to enhance cognition-driven activity paradigm by using the data-driven method. The methodology consists of clustering activity where basic partial activity models established through management technologies. By using this find out action cluster that denotes activities and accumulates recent actions. A learning activity is next formed to study and designing alternating methods of activities after obtain new finalize and specialized activity paradigms. This can be tested with sensor dataset and sensor dataset with noisy. Applications: It is mainly applicable for home-based rehabilitation, monitoring human activity and security-based applications.