Lightweight context-aware activity recognition
Document Type
Conference Proceeding
Source of Publication
Lecture Notes in Electrical Engineering
Publication Date
1-1-2016
Abstract
© Springer-Verlag Berlin Heidelberg 2016. 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.
DOI Link
ISBN
9783662478943
ISSN
Publisher
Springer Verlag
Volume
354
First Page
367
Last Page
373
Disciplines
Computer Sciences
Keywords
Activity recognition, Knowledgebase, Ontology
Scopus ID
Recommended Citation
Go, Byung Gill; Khattak, Asad Masood; Shah, Babar; and Khan, Adil Mehmood, "Lightweight context-aware activity recognition" (2016). All Works. 2258.
https://zuscholars.zu.ac.ae/works/2258
Indexed in Scopus
yes
Open Access
no