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

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

This publication comprises a few chapters addressing assorted points of task popularity, approximately in 3 major different types of issues. the 1st subject should be all for task modeling, illustration and reasoning utilizing mathematical versions, wisdom illustration formalisms and AI recommendations. the second one subject will be aware of task attractiveness tools and algorithms. except conventional tools according to information mining and desktop studying, we're fairly drawn to novel methods, similar to the ontology-based process, 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. furthermore, this subject also will contain the underpinning technological infrastructure, i.e. instruments and APIs, that helps function/capability sharing and reuse, and speedy improvement and deployment of technological options. The fourth class of subject can be devoted to consultant purposes of task popularity in clever environments, which tackle the lifestyles cycle of task attractiveness and their use for novel features of the end-user structures with complete implementation, prototyping and evaluate. it will comprise quite a lot of program situations, akin to clever houses, clever convention venues and cars.

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In the kitchen various sensors are attached to objects pertaining to ADLs. For example, contact sensors are attached to the entry door, containers of teabag, sugar, milk, coffee and chocolate, and a cup. The activation of a sensor indicates the occurrence of an action involving the object to which the sensor is attached. The event will be interpreted Activity Recognition: Approaches, Practices and Trends Fig. 3 19 A fragment of the SH context and ADL ontologies and mapped to a corresponding concept or property in the SH ontologies giving specific contextual information.

Lin Z. , Multi-user preference model and service provision in a smart home environment, in: Proc. of IEEE International Conference on Automation Science and Engineering (CASE 2007), 2007. , Sensor-Based Human Activity Recognition in a Multi-user Scenario, AmI 2009, LNCS 5859, pp. 78–87, 2009. , Recognizing independent and joint activities among multiple residents in smart environments. Ambient Intelligence and Humanized Computing Journal, 1(1):57–63, 2010. , homeML - An open standard for the exchange of data within smart environments, Proceedings of 5th International Conference on Smart homes and health Telematics, Lecture Notes in Computer Science (Vol.

Fogarty et al. [63] deploy a small number of low-cost sensors at critical locations in a home’s existing water distribution infrastructure. The authors infer activities in the home based on water usage patterns. These systems would be useful to explore this direction. 4 Abnormal activity recognition The existing systems recognize activities in various levels such as action level, ADL (Activity of Daily Living) level, and high level. But they all are normal activities. Another kind of activity that we should pay attention is abnormal activity.

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