Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

Context-driven personalized service discovery in pervasive environments

Abstract: Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. User context is often rich and very dynamic; making an explicit, user-driven discovery of services impractical. Users in such environments would instead like to be continuously informed about services relevant to them. Implicit discovery requests triggered by changes in the context are therefore prevalent. This paper proposes a proactive service discovery approach for pervasive environments addressing these implicit requests. Services and user preferences are described by a formal context model called Hyperspace Analogue to Context, which effectively captures the dynamics of context and the relationship between services and context. Based on the model, we propose a set of algorithms that can continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied to improve the algorithms' performance. The algorithms are grounded in a context-driven service discovery system that automatically reacts to changes in the environment. New context sources and services can be dynamically integrated into the system. A client for smart phones continuously informs users about the discovery results. Experiments show, that the system can efficiently provide the user with continuous, up-to-date information about the most useful services in real time.

Collision-free human-robot collaboration based on context awareness

Abstract: Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.

Modelling Context: An activity theory approach

Abstract: In this paper, we review different context classification systems that have been used to define elements of context. Although existing classification systems cover various types of context, in the development of context aware applications, only a few types of context have been used. In this work, we aim to build a context classification model based on Activity Theory that provides a basis both for dialogue amongst context awareness researchers and for the implementation of a context awareness architecture.

Mobile Context Aware Systems: The Intelligence to Support Tasks and Effectively Utilise Resources

Abstract: The complex usage of mobile devices coupled with their limited resources in terms of display and processing suggests that being able to understand the context of the user would be beneficial. In this paper we present a model that describes context as a dynamic process with historic dependencies. We also describe software architecture to support this model, and evaluate its effectiveness in a mobile learning scenario. Preliminary results from our evaluation suggest important issues for consideration in the continuing development of context aware systems and interfaces, including the need for appropriate representation of contextual data to the user, and maintaining a balance between effective support and intrusion.