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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.
Activity Recognition, Activity Paradigm, Cognition-Driven, Data-Driven.
- Choudhury T, Consolvo S. The mobile sensing platform: An embedded activity recognition system. Institute of Electrical and Electronics Engineers (IEEE) Pervasive Computing. 2008 Apr- Jun; 7(2):32–41.
- Philipose M, Fishkin K. Inferring activities from interactions with objects. Institute of Electrical and Electronics Engineers (IEEE) Pervasive Computing. 2004 Oct; 3(4):50–7.
- Caballero AF. Human activity monitoring by local and global finite state machines. Expert Systems with Applications.2012 Jun; 39(8):6982–93.
- Laerhoven KV, Aidoo K. Teaching context to applications.Personal and Ubiquitous Computing. 2001 Feb; 5(1):46–9.
- Chen L, Hoey J, Nugent C, Cook D, Yu Z. Sensor-based activity recognition. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Systems, Man, and Cybernetics. 2012 Nov; 42(6):790–808.
- Bao L, Intille S. Activity recognition from user-annotated acceleration data. Springer Berlin Heidelberg; 2004 Apr.p. 1–17.
- Bouchard B, Giroux S, Bouzouane A. A smart home agent for plan recognition of cognitively-impaired patients.Journal of Computers. 2006 Aug; 1(5):53–62.
- Azkune G, Almeida A, de Ipina DL, Chen L. A knowledgedriven tool for automatic activity dataset annotation.Springer International Publishing; 2014 Sep. p. 593–604.
- Anuradha K, Sairam N. Spatio-temporal based approaches for human action recognition in static and dynamic background: a survey. Indian Journal of Science and Technology.2016 Feb; 9(5):1–12.
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