Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array

Abstract

Abstract-Context-aware computing describes the situation where a wearable/mobile computer is aware of its user's state and surroundings and modifies its behavior based on this information. We designed, implemented, and evaluated a wearable system which can learn context-dependent personal preferences by identifying individual user states and observing how the user interacts with the system in these states. This learning occurs online and does not require external supervision. The system relies on techniques from machine learning and statistical analysis. A case study integrates the approach in a context-aware mobile phone. The results indicate that the method is able to create a meaningful user context model while only requiring data from comfortable wearable sensor devices.

Providing user context for mobile and social networking applications

“…The application layer has been developed using the Java ME platform 13 , a technology that is widely used due to its recognized portability across many mobile phone devices. The mobile phone runs a proprietary operating system which supports J2ME MIDlets.…”

“…The mobile phone runs a proprietary operating system which supports J2ME MIDlets. With the help of a Mobile Information Device Profile (MIDP), the 13 http://java.sun.com/javame/ application acquires raw sensor data from both the internal sensors and external sensor nodes, namely the BlueSentry aggregating node and a bluetooth GPS sensor.…”

Using Description Logics for the Provision of Context-Driven Content Adaptation Services

“…A number of context-based adaptation methods (Lei & Georganas 2001;Lum & Lau 2002;Pashtan, Kollipara et al 2003;Toivonen, Kolari et al 2003;Kurz, Popescu et al 2004;Lemlouma & Layaida 2004;Krause, Smailagic et al 2006) are proposed to customize Web content according to client contextual environments, including personal preferences, device capabilities, and access environments. Julien and Roman (Julien & Roman 2006) introduce a view concept to represent application-specific contextual information.…”

“…Mukherjee et al (Mukherjee, Delfosse et al 2005) take into consideration run-time conditions about terminal, network, user preference, and rights. Krause et al (Krause, Smailagic et al 2006) believe that contextual information includes users states and surroundings. A user's state can be extracted from the user's activity, location, schedule, and physiological information.…”

Modular Bayesian Networks for Inferring Landmarks on Mobile Daily Life

“…A. Krause, et al clustered sensor and log data collected on mobile devices, learned a context classifier that reflected user's preference, and estimated user's situation to provide smart services to the user [2]. E. Horvitz, et al proposed a method that detected and estimated landmarks by learning human's cognitive activity model from PC log data based on Bayesian approach [1].…”