Context awareness

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

Location history in a low-cost context awareness environment

Abstract: Location awareness is a crucial part of the context-awareness mechanism for ubicomputing. This paper explores how usefull is the location awareness history for an office based low-cost context-awareness environment. Capturing location awareness data into a relational database is simple and feasible in office environment. We use extended SQL to access the location awareness history database to provide direct support for speech commands. The mechanism improve flexibility for developing context awareness application in the Intelligent Environment.

Exploiting the icon arrangement on mobile devices as information source for context-awareness

Abstract: The contextual relevance of a service can only be determined by the human himself. However, a measure for relevance is required for context-aware service delivery. In this paper, we draw attention to icon arrangement on mobile devices as a new source of information for adaptive menus. We conducted contextual inquiries to investigate how people arrange icons on a grid-based menu. Our results show that context has an impact on how users arrange their menus: during different activities they prefer different icons to be placed at specific positions. We discuss layout options for icon menus and argue how the relevance can be approximated by observing the icon arrangement. Our results informed the design of a context-aware client for mobile services, which is presented as a prototype.

Towards Context Awareness Using Symbol Clustering Map

Abstract: Recognizing the context of use is important in making mobile devices simple to use The device and the underlying mobile service can provide a personalized user interface that adapts to the usage situation The device can infer parts of the context of the user from features extracted from on-board measurements of acceleration, noise level, luminosity, humidity, etc In this paper we consider context recognition by fusing and clustering these context features using a recently introduced method, the Symbol Clustering Map As such, it can be used for finding static patterns but a suitable transformation of the data allows identifying also temporal patterns

Dempster–Shafer theory-based human activity recognition in smart home environments

Abstract: Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naive Bayes classifier and J48 decision tree.

Going my way: a user-aware route planner

Abstract: Going My Way is a mobile user-aware route planner. The system collects GPS data of a user's everyday locations and provides directions from an automatically selected set of landmarks that are close to the destination, informed by the user's usual travel patterns. In this paper, we present a brief description of the system, the results of a preliminary experiment in memory and recognition of landmarks, in addition to the results of a user evaluation of the system.