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Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the(More)
—Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality(More)
—dinam is a novel approach to simplified rapid prototyping of wireless sensor network applications as well as an according WSN platform. As opposed to the traditional mote-based development archetype, dinam proposes combining the development steps into a single continuous, fluid process that is completely integrated into the node. The dinam concept sensor(More)
This paper researches the potential of a novel ball switch as a wearable vibration sensor for activity recognition. The ball switch is available as a commercial, off-the-shelf sensor and is unique among such sensors due to its minia-turized design and the low mass of the ball. We present a detailed analysis of the physical properties of the sensor as well(More)
During the vast trend of urbanization, mobile sensing in metropolitan area has become an emerging fashion and prevailing technology to monitor the environmental changes and human activities in the city scale. In this paper, we propose a novel framework, namely, the Context-Aware Metropolitan Sensing (CAMS), to rise to the increasing challenges in context(More)
Energy storage is quickly becoming the limiting factor in mobile pervasive technology. For intelligent wearable applications to be practical, methods for low power activity recognition must be embedded in mobile devices. We present a novel method for activity recognition which leverages the predictability of human behavior to conserve energy. The novel(More)
Pervasive computing envisions implicit interaction between people and their intelligent environments instead of between individuals and their devices, inevitably leading to groups of individuals interacting with the same intelligent environment. These environments must be aware of user contexts and activities, as well as the contexts and activities of(More)
This paper examines global context classification in peer-to-peer ad-hoc mobile wireless networks (P2P-MANETs). To begin, circumstances are presented in which such systems would be required to classify a global context. These circumstances are expounded upon by presenting concrete scenarios from which a set of requirements are derived. Using these(More)
—As devices are expected to be aware of their environment, the challenge becomes how to accommodate these abilities with the power constraints which plague modern mobile devices. We present a framework for an embedded approach to context recognition which reduces power consumption. This is accomplished by identifying class-sensor dependencies, and using(More)