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WMC19 - Technical Session 4 - WBANs and eHealth

Title: A Wireless Body Area Networks transmission scheduler based on human body movements

Author: Vinicius Correa Ferreira


Abstract: Advances in electronics have enabled the development of intelligent miniaturized biomedical sensors that can be used to monitor the human body. The use of wireless communication proved to be an alternative, which provides less discomfort to patients and good cost-benefit. In order to fully exploit the benefits of wireless technologies in telemedicine, a new type of wireless networks has emerged: Wireless Body Area Networks (WBANs). However, technical and social challenges must be addressed to enable their adoption. Some factors such as the use of the human body as a propagation media, the effects of radiation on human tissue and the human body movement, make WBANs a new paradigm of wireless communication networks. To meet the requirements of WBAN applications, while preserving the energy efficiency and the user's physical safety, this paper proposes a transmission scheduler based on the movement of the human body. Improvements in the packet delivery rate, and energy efficiency are observed when compared to polling and random media access (CSMA/CA).




Title: Simulation of ISO/IEEE 11073 Personal Health Devices in WBANs

Author: Robson Araújo Lima


Abstract: Simulating new protocols for e-health systems is very important, as it allows an initial evaluation before a real implementation is made. On the other hand, network simulators do not offer proper support to represent medical applications or components to facilitate running simulations modeling e-health applications. The lack of simulators that specify the sensor type and its communication requirements make real experiments harder. Aiming at fulfilling this gap, this paper proposes the use of ISO/IEEE 11073 standard for Personal Health Devices (X73-PHD) in e-health network simulations, representing realistic medical applications and investigating the behavior of medical devices (sensors or actuators) in Wireless Body Area Network (WBAN) scenarios. We developed a free and open-source implementation of X73-PHD for Castalia Simulator, providing five different PHD types to act like real ISO/IEEE11073 devices in WBAN simulations. Our implementation supportsAgent-initiated mode, where PHDs take the initiative to send measurements to the hub. Our implementation also supports the unconfirmed communication mode and the confirmed communication mode, where the receiver sends an acknowledgment to the sender every time it receives a packet. Simulation results showed that the confirmed communication mode did not perform well in WBANs when the interval between transmissions is too small, due to the long period of timeout proposed in the X73-PHD standard. There-fore, we propose a new extension to the confirmed mode standard that decreases the overhead of control packets over the network, using smaller timeouts and delivering more pac



Title: Identifying Post-Traumatic Stress Symptoms Using Physiological Signals and Artificial Intelligence

Author: Luiz Antonio da Ponte Junior


Abstract: The number of people diagnosed with an anxiety disorder has increased. The correct diagnosis of such disorders is not always a trivial task, forcing the individual often consulting with many clinicians and performing several medical exams. Post-traumatic Stress Disorder (PTSD) is a disorder related to experienced events, which presented a certain degree of threat to an individual. When experiencing situations that refer to past events, an individual may present reactions that trigger physiological changes in his/her organism such as tachycardia or bradycardia. Many disorders have common symptoms, and realizing these subtleties results in the diagnosis' efficiency and effectiveness. Artificial Intelligence (AI) techniques has helped specialists in the diagnosis and prevention of diseases and disorders, accelerating the process and increasing its effectiveness. In this paper, we aim at finding new biomarkers to diagnose PTSD analyzing physiological signals with AI techniques. We used a dataset from an experiment with civilians that were recently exposed to traumatic events related to violence. Those individuals completed a questionnaire that evaluates the impact of such events through PCL (PTSD Disorder Checklist for DSM-IV) scale. Heart rate and skin conductance signals were collected while viewing emotional and neutral stimuli images. We applied data mining techniques and classification algorithms to evaluate and maximize PCL score prediction performance considering those physiological signal data. The best result was obtained with Naive Bayes algorithm, after applying supervised discretization and attribute selection, presenting an accuracy of 96.36% (p-value = 0.001), F-Measure of 0.9636 and AUC (Area Under ROC Curve) of 0.9681.




Title: Digital Cardiology: A mobile app to support the preparation for the exam 18-FDG PET CT for infection endocarditis patient

Author: Celine Soares


Abstract: The Positron Emission Tomography to cardiac examinations has been more utilized as a part of an image repertoire. A recurrent issue in the PEC-CT preparation is the need for a reduction of the glucose capture by the myocardium. In order to achieve proper suppression and, consequently, increased accuracy, it's mandatory to modify the diet three days before the 18FFDG CT. Under the auspices of a multidisciplinary team, we are developing with the Ionic4 framework for multiplatform, a mobile application able to register and inform the user of the proper diet that the patient should perform for three days before the exam. Once installed on smartphones It is planned to analyze the mobile app implementation and to evaluate if the project is able to offer practicality, the democratization of access and provide the quality indicators to managers.

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