3 research outputs found

    Smart system for children's chronic illness monitoring

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    [EN] Sick children need a continuous monitoring, but this involves high costs for the government and for the parents. The use of information and communication technologies (ICT) jointly with artificial intelligence and smart devices can reduce these costs, help the children and assist their parents. This paper presents a smart architecture for children's chronic illness monitoring that will let the caregivers (parents, teachers and doctors) to remotely monitor the health of the children based on the sensors embedded in the smartphones and smart wearable devices. The proposed architecture includes a smart algorithm developed to intelligently detect if a parameter has exceeded a threshold, thus it may imply an emergency or not. To check the correct operation of this system, we have developed a small wearable device that is able to measure the heart rate and the body temperature. We have designed a secure mechanism to stablish a Bluetooth connection with the smartphone. In addition, the system is able to perform the data fusion in both the information packetizing process, which contributes to improve the protocol performance, and in the measured values combination, where it is used a stochastic approach. As a result, our system can fusion data from different sensors in real-time and detect automatically strange situations for sending a warning to the caregivers. Finally, the consumed bandwidth and battery autonomy of the developed device have been measured.This work has been partially supported by the "Ministerio de EducaciOn, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Sendra, S.; Parra-Boronat, L.; Lloret, J.; Tomás Gironés, J. (2018). Smart system for children's chronic illness monitoring. Information Fusion. 40:76-86. https://doi.org/10.1016/j.inffus.2017.06.002S76864

    Generalized Activity Assessment computed fully distributed within a Wireless Body Area Network

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    Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-)learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment

    A Task-Oriented Framework for Networked Wearable Computing

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    Body Sensor Networks (BSNs) have become prominent in research and industry alike as a powerful enabler of novel applications in human-centered domains. However, developing applications on such systems is still a cumbersome process, due to the lack of suitable software abstractions and the difficulties in managing wearable computing application within the stringent constraints of embedded systems. In this paper, we introduce a novel framework, SPINE2 (Signal Processing In Node Environment), which allows task-oriented programming on a platform-independent architecture. We demonstrate how fairly sophisticated signal-processing applications can be realized in the form of easy-to-implement embedded processes. The proposed architecture is tested experimentally and its features are illustrated through a nontrivial case study
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