Download Activity Recognition in Pervasive Intelligent Environments by Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey PDF

By Liming Chen, Chris D. Nugent, Jit Biswas, Jesse Hoey

This ebook involves a few chapters addressing varied facets of job attractiveness, approximately in 3 major different types of issues. the 1st subject might be concerned with task modeling, illustration and reasoning utilizing mathematical versions, wisdom illustration formalisms and AI strategies. the second one subject will be aware of job popularity tools and algorithms. except conventional tools in line with info mining and computer studying, we're relatively attracted to novel methods, similar to the ontology-based technique, that facilitate information integration, sharing and automatic/automated processing. within the 3rd subject we intend to hide novel architectures and frameworks for job reputation, that are scalable and acceptable to giant scale allotted dynamic environments. additionally, this subject also will contain the underpinning technological infrastructure, i.e. instruments and APIs, that helps function/capability sharing and reuse, and swift improvement and deployment of technological ideas. The fourth class of subject might be devoted to consultant purposes of job popularity in clever environments, which handle the existence cycle of task attractiveness and their use for novel features of the end-user platforms with finished implementation, prototyping and assessment. this can contain quite a lot of program situations, reminiscent of clever houses, clever convention venues and cars.

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Additional resources for Activity Recognition in Pervasive Intelligent Environments

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OWL or RDF [40] for specifying activities, and their descriptors and relationships. , objects, environment elements and events, facilitates interoperability, reusability and portability of the models between different systems and application domains. 3 Semantic sensor metadata creation In a SH sensor data are generated continuously, and activity assistance needs to be provided dynamically, both along a timeline. This requires that semantic enrichment of sensor data should be done in real time so that the activity inference can take place.

2000), Learning Patterns of Activity Using Real-Time Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 747– 757. , (2003), Vision based human tracking and activity recognition, In Proceedings of the 11th Mediterranean Conference on Control and Automation. , (2008), Multi-Camera Human Activity Monitoring, Journal of Intelligent and Robotic Systems, Vol. 52, No. 1, pp. 5–43. [7] Bao L. , (2004), Activity recognition from userannotated acceleration data, In 28 Activity Recognition in Pervasive Intelligent Environments Proc.

E. DL Query tab through which complex query expressions can be framed. Fig. 4 shows the implementation process of the ADL recognition. We first input the situational context described above into the class expression pane using a simplified OWL DL query syntax. When the Execute button is pressed, the specified context is passed onto the backend FaCT++ reasoner to reason against the ontological ADL models. The results which are returned are displayed in the Super classes, Sub classes, Descendant classes, Instances and Equivalent classes panes, which can be interpreted as follows: • If a class in the Super classes pane is exactly the same as the one in the Sub classes pane, then the class can be regarded as the ongoing ADL.

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