Abstract. In this paper we demonstrate how a cell phone can infer contextual information such as mode of travel by monitoring the fluctuation of GSM signal strength levels and neighbouring cell information. We show that these signals are stable enough to reliably distinguish between various states of movement such as walking, travelling in a motor car and remaining still. We present preliminary results for a metropolitan environment. 1

Abstract—This paper deals with a new medical information system called Alarm Net designed for smart healthcare. Based on an advanced Wireless Sensor Network (WSN), it specifically targets assisted-living residents and others who may benefit from continuous and remote health monitoring. We present the advantages, objectives, and status of the system built at the Department of Computer Science at UVA. Early results of the prototype suggest a strong potential for WSNs to open new research perspectives for ad hoc deployment of multi-modal sensors and improved quality of medical care. A

Abstract not found

In this paper we explore the use of text processing technology for on-line assessment in an engineering design project class. We present results from a 5-week classroom study in a capstone engineering design course in which we explore the potential benefits of such technology for student learning in this context. Furthermore, we present results from ongoing work assessing student productivity based on features extracted from their conversational behavior in the course discussion board. While we found that typical shallow productivity measures such as number of posts, length of posts, or number of files committed have no correlation with an instructor assigned grade, we can achieve a substantial improvement using simple linguistic patterns extracted from on-line conversational behavior.

Abstract—This paper presents an application of a service-based architecture to pervasive monitoring of the elderly using ambient and wearable sensors. The design consideration of the model addresses heterogeneous computing and network resource utilization, allowing inter-operability and supporting dynamic environments to achieve system wide resource optimization. An application of this architecture is presented for assessing the activities of daily living, which is the basis for pervasive sensing for elderly care.

A learning context describes the current situation of a learner related to a learning activity, and continually changes in a mobile learning environment. As a result, context-awareness in mobile learning has become an essential part when designing more adaptive mobile learning systems. In this paper, we introduce our novel Contextaware and Adaptive Learning Schedule framework which makes use of a learning schedule to support the students ’ daily routines, adapts the activities to the student’s learning styles and then selects the appropriate activity for the learner based on their current learning context.

in a pervasive environment

using temporal features to create more

acceptée sur proposition du jury: Prof. S. Süsstrunk, présidente du jury Prof. D. Floreano, directeur de thèse Prof. J. d. R. Millán, rapporteur

Purpose – Mobile computing research has been focused on developing technologies for handheld devices such as mobile phones, notebook computers, and mobile IP. Today, emphasis is increasing on context-awarecomputing, which aims tobuildtheintelligence intomobiledevicestosenseandrespondtotheuser’s context.Thepurposeofthispaper is thus to present a context-aware mobile computing model (ContextAlert) that senses the user’s context and intelligently configures the mobile phone alert mode accordingly. Design/methodology/approach – The paper proposes a three-step approach in designing the model based on the embedded sensor data (accelerometer, GPS antenna, and microphone) of a G1 Adriod phone. As adaptivity is essential for context-aware computing, within this model a new learning mechanism is presented to maintain a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal. Findings – The model has been evaluated in many aspects using data collected from human subjects. The experiment results show that the proposed model performs well and yields a promising result. Originality/value – This paper is distinguished from other previous papers by: (i) using multiple sensors embeded in the mobile phone, which is more realistic for detecting the user’s context than having various sensors attached to different parts of user’s body, (ii) a novel model that uses sensed contextual information to provide a service that better synchronizes the user’s daily life with a context-aware alert mode. With this service, the user can avoid the problems such as forgetting to switch to