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Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different(More)
Automated activity recognition enables a wide variety of applications related to child and elderly care, disease diagnosis and treatment, personal health or sports training, for which it is key to seamlessly determine and log the user’s motion. This work focuses on exploring the use of smartphones to perform activity recognition without interfering in the(More)
— Wireless Sensor Networks (WSN) based on ZigBee/IEEE 802.15.4 will be key enablers of non-invasive, highly sensitive infrastructures to support the provision of future ambient assisted living services. This paper addresses the main design concerns and requirements when conceiving Ambient Care Systems (ACS), frameworks to provide remote monitoring,(More)
Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system(More)
This paper describes a context-aware mobile application which aims at adaptively motivating its users to assume active lifestyles. The application is built on a model which combines 'motion patterns' with 'activity profiles', in order to evaluate the user's real level of activity and decide which actions to take to give advice or provide feedback. In(More)
Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a(More)
This paper describes experiences and lessons learned in applying an approach of using a physical robot for testing overall smartphone device performance based on user profiles. The process consists of capturing user actions, abstracting them to usage profiles, transforming these into test models, and generating test cases from the models. The goal is to(More)
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