Abstract
In ubiquitous environments, it is important to recognize the situation and deliver services accordingly. In addition, it is equally important to have a fast response time. The existing context-aware activity recognition engines have good recognition rates; however, they consume lots of time to produce feasible results. Our focus in this research is to reduce the time required by eliminating the need for ontology matching (in context-aware activity manipulation engine) and extend the rules. In addition, we incorporate the sliding time window concept to retain activities for a longer duration and maintain their relevance using ontological data for a better accuracy. The proposed scheme has increased the overall accuracy against the existing system by 12.6 % for individual activities relevance and 6 % for high level activities.
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Go, B.G., Khattak, A.M., Shah, B., Khan, A.M. (2016). Lightweight Context-Aware Activity Recognition. In: Park, J., Chao, HC., Arabnia, H., Yen, N. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47895-0_44
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DOI: https://doi.org/10.1007/978-3-662-47895-0_44
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