O efeito realidade virtual na reabilitação da marcha e equilíbrio em pacientes após AVE fase crónica: uma revisão bibliográfica

Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciada em FisioterapiaIntrodução: Os sistemas de jogos de realidade virtual (RV) oferecem a oportunidade de trabalhar com técnicas baseadas em tarefas através um ambiente estimulante, divertido e seguro, garantindo interesse e motivação nos pacientes afetos por acidente vascular encefálico (AVE), treinando a cognição, melhoria motora e estimulando a reorganização cortical. Objetivos: O objetivo deste estudo foi o de analisar o efeito da realidade virtual como recurso de reabilitação da marcha e no equilíbrio postural em pacientes com AVE crónico. Metodologia: Pesquisa de estudos de datados até o 2021 nas bases de dados Web of Science, PEDro e PubMed, inserindo as palavras-chave: Stroke, Virtual Reality, Gait, Balance com as seguintes combinações: “Stroke” AND “Virtual Reality” AND “Gait” OR “Balance”. Em particular na PEDro, foi inserido apenas “Virtual Reality* Stroke* Gait* Balance*”. A qualidade metodológica dos estudos foi analisada através da Escala de PEDro, obtendo uma média de 6/10. Resultados: Nesta revisão foram incluídos 6 artigos com um total de 152 indivíduos entre os 40 e 70 anos. Conclusão: o treino em RV parece ser eficaz na melhoria do equilíbrio e da marcha em pacientes com AVE numa fase crónica quando combinada com a terapia convencional.Introduction: Virtual reality (VR) gaming systems offer the opportunity to work with task- based techniques through a stimulating, fun and safe environment, ensuring interest and motivation in patients affected by stroke (CVA), training cognition, motor improvement and stimulating cortical reorganization. Objectives: The objective of this study is to analyze the effect of virtual reality technology on gait rehabilitation and postural balance in patients with chronic stroke. Methodology: Studies dated until 2021 in the databases Web of Science, PEDro and Pubmed, inserting the keywords: Stroke, Virtual Reality, Gait, Balance in the following combinations: "Stroke" AND "Virtual Reality" AND "Gait" OR "Balance". In particular in PEDro, only "Virtual Reality* Stroke* Gait* Balance* was inserted". The methodological quality of the studies was analyzed using the Pedro Scale, obtaining an average of 6/10 (Figure 1). Results: This review included 6 articles with a total of 152 individuals between 40 and 70 years. Conclusion: VR training seems to be effective in improving balance and gait in stroke patients in a chronic phase when combined with conventional therapy.N/

A Novel Echo State Network Autoencoder for Anomaly Detection in Industrial Iot Systems

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, Echo State Networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this paper, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best trade-off in terms of memory footprint and inference time

A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

An IoT Testbed for the Software Defined City Vision: The #SmartMe Project

© 2016 IEEE.To kickstart the process of morphing Messina into a «smart» city, an explicit mission for the crowdfunded #SmartME project, it is essential to set up an infrastructure of smart devices embedding sensors and actuators, to be scattered all over the urban area. An horizontal framework coupled with the Fog computing approach, by moving logic toward the «extreme» edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed, is a suitable solution for data- intensive services with time-bound constraints as those usually required by citizens. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. We present some of the core Stack4Things functionalities implementing a Fog computing approach towards a run- time «rewireable» Smart City paradigm, by outlining node management and contextualization mechanisms, also describing its usage in terms of already supported and developed verticals, as well as a specific example related to environmental data collection through #SmartME

IoT-cloud authorization and delegation mechanisms for ubiquitous sensing and actuation

© 2016 IEEE.In the roadmap for the implementation of ubiquitous computing, ubiquitous sensing and actuation is a milestone still to be reached. It refers to providing sensing and actuation facilities anytime and everywhere. This does not just imply to interconnect sensors and actuators through the Internet, but also and mainly to provide this facilities. IoT-Cloud computing paradigms such as the sensing and actuation as a service one could be a proper way to address this problem. In past work we developed an SAaaS framework extending OpenStack with specific functionalities for resource constrained nodes, Stack4Things. In this paper we focus on access control, authorization and delegation mechanisms which are basic mechanisms for the implementation of the UbSA vision. Thus starting from Stack4Things, we describe how we adapted and extended mechanisms provided by OpenStack, with specific regard to Keystone, with new functionalities for delegation and access control. A use case in the smart city scenario of #SmartME describes the proposed solution in practice

Dependability modeling of Software Defined Networking

© 2015 Elsevier B.V. Software Defined Networking (SDN) is a new network design paradigm that aims at simplifying the implementation of complex networking infrastructures by separating the forwarding functionalities (data plane) from the network logical control (control plane). Network devices are used only for forwarding, while decisions about where data is sent are taken by a logically centralized yet physically distributed component, i.e., the SDN controller. From a quality of service (QoS) point of view, an SDN controller is a complex system whose operation can be highly dependent on a variety of parameters, e.g., its degree of distribution, the corresponding topology, the number of network devices to control, and so on. Dependability aspects are particularly critical in this context. In this work, we present a new analytical modeling technique that allows us to represent an SDN controller whose components are organized in a hierarchical topology, focusing on reliability and availability aspects and overcoming issues and limitations of Markovian models. In particular, our approach allows to capture changes in the operating conditions (e.g., in the number of managed devices) still allowing to represent the underlying phenomena through generally distributed events. The dependability of a use case on a two-layer hierarchical SDN control plane is investigated through the proposed technique providing numerical results to demonstrate the feasibility of the approach

Stack4Things: a sensing-and-actuation-as-a-service framework for IoT and cloud integration

© 2016, Institut Mines-Télécom and Springer-Verlag France. With the increasing adoption of embedded smart devices and their involvement in different application fields, complexity may quickly grow, thus making vertical ad hoc solutions ineffective. Recently, the Internet of Things (IoT) and Cloud integration seems to be one of the winning solutions in order to opportunely manage the proliferation of both data and devices. In this paper, following the idea to reuse as much tooling as possible, we propose, with regards to infrastructure management, to adopt a widely used and competitive framework for Infrastructure-as-a-Service such as OpenStack. Therefore, we describe approaches and architectures so far preliminary implemented for enabling Cloud-mediated interactions with droves of sensor- and actuator-hosting nodes by presenting Stack4Things, a framework for Sensing-and-Actuation-as-a-Service (SAaaS). In particular, starting from a detailed requirement analysis, in this work, we focus on the subsystems of Stack4Things devoted to resource control and management as well as on those related to the management and collection of sensing data. Several use cases are presented justifying how our proposed framework can be viewed as a concrete step toward the complete fulfillment of the SAaaS vision

Personalized Health Tracking with Edge Computing Technologies

© 2016, Springer Science+Business Media New York.The health monitoring component is the essential block, a pillar of several e-health systems. Plenty of health tracking applications and specific technologies such as smart devices, wearables, and data management systems are available. To be effective, promptly reacting to issues, a health monitoring service must ensure short delays in data sensing, collection, and processing activities. This is an open problem that distributed computing paradigms, such as Internet of Things (IoT), Cloud, and Edge computing, could address. The solution proposed in this paper is based on Stack4Things, an IoT-Cloud framework to manage edge nodes such as mobiles, smart objects, network devices, workstations, as a whole, a computing infrastructure allowing to provide resources on-demand, as services, to end users. Through Stack4Things facilities, the health tracking system can locate the closer computing resource to offload processing and thus reducing latency per the Edge computing paradigm

Cloud-based network virtualization: An IoT use case

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. In light of an overarching scheme about extending the capabilities of Internet of things (IoT) with Cloud-enabled mechanisms, network virtualization is a key enabler of infrastructure-oriented IoT solutions. In particular, without network virtualization infrastructure cannot really be considered flexible enough to meet emerging requirements, and even administrative duties, such as management, maintenance and large-scale automation, would turn out to be brittle and addressed by special casing, leading to loss of generality and a variety of corner cases. We propose a Cloud-based network virtualization approach for IoT, based on the Open- Stack IaaS framework, where its networking subsystem, Neutron, gets extended to accomodate virtual networks and arbitrary topologies among virtualmachines and globally dispersed smart objects, whichever the setup and constraints of the underlying physical networks. This work outlines a motivating use case for our approach, and the ensuing discussion is provided to frame the benefits of the underlying design

Hospitalized Patient Monitoring and Early Treatment Using IoT and Cloud

© 2016, Springer Science+Business Media New York.The adoption of Internet of Things devices and, more in general, embedded systems, endowed with sensors and actuators, keeps rising globally, and the scope of their involvement broadens, for instance in e-Health applications. This work describes our integration of IoT paradigms and resource ecosystems with a tailored Cloud-oriented device-centric environment, by focusing on an e-Health scenario, featuring monitoring and early treatment of hospitalized patients, by focusing on Cloud-enabled event detection coupled with coordinated reaction