ESTUDIO DE REDES DE SENSORES Y APLICACIONES ORIENTADAS A LA RECOLECCIÓN Y ANÁLISIS DE SEÑALES BIOMÉDICAS.

RESUMEN ANALÍTICOEl presente estudio realiza un análisis de las principales investigacionesque en los últimos años comprenden el uso de las redes de sensores inalámbricas (WSN) para aplicaciones médicas ydecuidado de la salud, con énfasis en lo relacionado con la captura y envío de datos en tiempo real, teniendo en cuenta la concepción de red WSN, sus protocolos y aplicaciones, como también eluso de sensores, sus clases y características;y la forma como se integran para hacer un sistema que apoye a la medicina mediante el uso de la tecnología en lo que generalmente se denominatelemedicina. Adicionalmente se ha tenido en cuenta que la información obtenida de la red de sensores es el insumo que permite el desarrollo de aplicaciones para el cuidado de la salud, la cuales un área de investigación que promete importantes oportunidades de investigación, por ello se ha realizado una revisión inicial del estado del arte en este tema. Por último, el estudio identifica losprincipales problemas y retos que presentan las aplicaciones propuestas en procura del análisis de dichas situaciones que a futuro pueden determinar nuevas investigaciones y desarrollos en el campode estudio.PALABRAS CLAVES: Telemedicina, Sensores, WSN, Minería de datos, Señales Biomédicas. ANALYTICAL SUMMARYThis survey aims to provide a review of the advancements in wireless sensor network (WSN) for health care applications. This includes the capture and real-time delivery of sensor data, the designof the WSN, its protocols, sensors, and its real-time requirements. In general, these kinds of systems are considered Telemedicine applications and have been the subject of extensive research. Theinformation collected by the WSN is the prime matter for the development of healthcare applications, anpromising area of research where we provide an initial review of the state of the art.Finally; thiswork presents a review of the main research problems in the area and outlines possible lines forfurther development.KEYWORDS: Telemedicine, Sensors, WSN, Data Mining, Biomedical Signals.

A Survey of Research on Health Monitoring System using Mobile Cloud Computing by Home Node Base Station

This paper presents a E-health monitoring scheme based on HNB (home node base station) and mobile cloud computing. In this method, the health data of each user is captured by using sensors and sent to the corresponding devices (i.e desktop, laptop, mobile). From that device the health data is transferred to cloud under which the mobile device is registered. In HNB it is verified whether the user’s health is normal using a database stored inside the HNB. If any abnormality is detected it will shows some indication through some sounds or light. The E-health data are send to cloud for each 15 seconds. In cloud also the data is verified with the normal data and if any abnormalities found it will indicate by sending message to the corresponding healthcare center. The health data in the cloud are stored with high security and only authentic healthcare center can access the data. Based on health data the healthcare centre takes proper action to cure the patient. DOI: 10.17762/ijritcc2321-8169.15027

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated

Industry 4.0 perspectives in the health sector in Brazil

Health 4.0 can be understood as the set of procedures that seek to improve the efficiency and speed of health professionals with possible guidelines for combining patient data in hospitals. However, systematizing and qualitatively describing the contributions of industry 4.0 in the context of the Brazilian health sector is a complex task. The aim of this paper is to present an analysis of industry 4.0 related to the health sector and its respective characteristics in Brazil. In addition, it discusses the prospects for greater use of technology in health care. In methodological terms, an exploratory field research was conducted with a non-random and intentional sample of professionals working in the technological context of Brazilian health. The research is classified as descriptive and qualitative, exploratory. The results contribute to narrow the information gap about industry 4.0 in the Brazilian health sector. The study allowed to develop a concept map of health 4.0 regarding the professional profile, considering the adoption of technologies that may favor the sector

Capítulo 1: Las redes de sensores inalámbricas, arquitectura y aplicaciones

Las redes de sensores inalámbricas (WSN), son una tendencia tecnológica que cada día cuenta con más aplicaciones, las características de sus nodos, los protocolos que utilizan y la versatilidad de sus configuraciones, las hacen como una opción importante dentro del mundo tecnológico. Actualmente no es extraño encontrar en un hospital una WSN que haga seguimiento a un paciente con una determinada patología; como tampoco es fuera de lo común que en algunas granjas tecnificadas, las WSN ayuden a controlar el cultivo de la plantas que allí se producen; también es posible que alguien que haya viajado recientemente en un sistema de transporte masivo, haya experimentado que el bus mostraba en una pantalla la ubicación del mismo y la próxima estación que cubriría, lo mismo sucede con los aeropuertos, los bancos, etc.; es decir son tantas las aplicaciones que hoy existen alrededor de esta tecnología que su estudio es casi obligado y un referente importante para el desarrollo de nuevas soluciones que propendan por el mejoramiento de las condiciones de vida de las personas. En el presente capitulo se hace una reseña de la definición de las WSN, sus orígenes y aplicaciones. Luego se estudia los elementos que hacen parte de una WSN, las características de los mismos y se profundiza en la descripción técnica que contiene este tipo de redes. Por último, se presenta un acercamiento de la aplicación de las WSN con el cuidado de la salud y se cierra el capítulo con la descripción de un caso específico, relacionado con una patología denominada “Preeclampsia”

Desenvolvimento de hardware modulado para condicionamento, digitalização e transmissão wireless de biossinais: eletrocardiograma, eletromiograma, saturação da oxigenação sanguínea e temperatura corporal

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáO mercado de sistemas vestíveis de monitoramento de sinais vitais (Wearable Health-Monitoring Systems – WHMS) teve um grande salto junto com os avanços em tecnologias de comunicação sem fio e miniaturização de componentes elétricos, possibilitando a integração desses aparelhos no acompanhamento de pacientes enfermos ou, ainda, como facilitador da aquisição de dados para um possível diagnóstico. Neste sentido, esse trabalho visa o desenvolvimento de um WHMS estruturado em blocos para aquisição, condicionamento e envio wireless de biossinais, nomeadamente, eletrocardiograma (ECG), eletromiograma (EMG), temperatura corporal e saturação de oxigénio no sangue (SpO2), de forma não-invasiva e com alta confiabilidade. As etapas de aquisição e condicionamento dos analógicos (ECG e sEMG) foram desenvolvidas utilizando componentes analógicos, parametrizados sob a área de interesse única de cada sinal, a qual foi determinada através de um estudo bibliográfico de cada comportamento. Ademais, para aferir a temperatura e SpO2, são utilizados sensores digitais, respetivamente, DS18B20 e MAX30102. Quanto ao envio dos dados, primeiramente os sinais analógicos são convertidos para sinais digitais utilizando o conversor analógico-digital de 12 bits embebido na placa de desenvolvimento ESP32 V4, além de ser tratada a utilização do conversor ADS1296 de 24 bits. Posteriormente, com as informações armazenada na memória do microcontrolador, os dados são agrupados em pacotes priorizando o processamento mais rápido entre envios, objetivando o mínimo de perdas de dados. Após o desenvolvimento, os resultados foram comparados com dispositivos validados, buscando uma confirmação do funcionamento, obtendo melhores resultados nas medições de relação sinal-ruído e correspondência com o sinal ideal esperado para os sinais de ECG e EMG utilizando os circuitos desenvolvidos. De mesmo modo, para o sinal de temperatura, obteve-se resultados altamente precisos e um comportamento em concordância com o equipamento validado, porém, para as medições de SpO2, os resultados obtidos com o MAX30102 não foram adequados, sendo, então, propostas medidas a serem estudadas almejando a melhoria deste resultado.The wearable vital signs monitoring systems (WHMS) market had a great growth along with the advances in wireless communication technologies and miniaturization of electrical components, allowing the integration of these devices in the monitoring of sick patients or even as a facilitator of data acquisition for a possible diagnosis. In this regard, this work seeks to develop a WHMS designed in blocks for acquisition, conditioning, and wireless transmission of biosignals, such as, electrocardiogram (ECG), electromyogram (EMG), body temperature and blood oxygen saturation (SpO2), in a non-invasive way and with high reliability. The steps of acquisition and conditioning of the analog signals (ECG and sEMG) were developed using analog components, parameterized under the unique area of interest of each signal, which was determined through a bibliographic study of each behaviour. Moreover, to measure temperature and SpO2, digital sensors were used, respectively, DS18B20 and MAX30102. As for the data transmission, firstly the analog signals were converted to digital signals using the analog-to-digital converter with 12 bits embedded on the development board ESP32 V4, as well as considered the use of the ADS1296 converter with 24 bits, then, with the information stored in the microcontroller memory, the data are packetized prioritizing the fastest processing between transmissions, aiming at the minimum loss of data. After the development, the results were compared with validated devices, searching for a confirmation of the performance, obtaining better results in the measurements of signal-to-noise ratio and correspondence with the expected ideal signal for the ECG and EMG signals using the developed circuit. In the same way, for the temperature signal, it was achieved highly precise results and a behaviour in accordance with the validated equipment, however, for the SpO2 measurements, the results obtained with MAX30102 were not adequate, therefore, measures are then proposed to be investigated with a keen interest in improving this result

CMOS Hyperbolic Sine ELIN filters for low/audio frequency biomedical applications

Hyperbolic-Sine (Sinh) filters form a subclass of Externally-Linear-Internally-Non- Linear (ELIN) systems. They can handle large-signals in a low power environment under half the capacitor area required by the more popular ELIN Log-domain filters. Their inherent class-AB nature stems from the odd property of the sinh function at the heart of their companding operation. Despite this early realisation, the Sinh filtering paradigm has not attracted the interest it deserves to date probably due to its mathematical and circuit-level complexity. This Thesis presents an overview of the CMOS weak inversion Sinh filtering paradigm and explains how biomedical systems of low- to audio-frequency range could benefit from it. Its dual scope is to: consolidate the theory behind the synthesis and design of high order Sinh continuous–time filters and more importantly to confirm their micro-power consumption and 100+ dB of DR through measured results presented for the first time. Novel high order Sinh topologies are designed by means of a systematic mathematical framework introduced. They employ a recently proposed CMOS Sinh integrator comprising only p-type devices in its translinear loops. The performance of the high order topologies is evaluated both solely and in comparison with their Log domain counterparts. A 5th order Sinh Chebyshev low pass filter is compared head-to-head with a corresponding and also novel Log domain class-AB topology, confirming that Sinh filters constitute a solution of equally high DR (100+ dB) with half the capacitor area at the expense of higher complexity and power consumption. The theoretical findings are validated by means of measured results from an 8th order notch filter for 50/60Hz noise fabricated in a 0.35μm CMOS technology. Measured results confirm a DR of 102dB, a moderate SNR of ~60dB and 74μW power consumption from 2V power supply

Security and Privacy for Mobile Social Networks

With the ever-increasing demands of people's social interactions, traditional online social networking applications are being shifted to the mobile ones, enabling users' social networking and interactions anywhere anytime. Due to the portability and pervasiveness of mobile devices, such as smartphones, wearable devices and tablets, Mobile Social Network (MSN), as a promising social network platform, has become increasingly popular and brought immense benefits. In MSN, users can easily discover and chat with social friends in the vicinity even without the Internet; vehicle drivers and passengers can exchange traffic information, videos or images with other vehicles on the road; customers in a shopping mall can share sale information and recommend it to their friends. With MSNs, massive opportunities are created to facilitate people's social interactions and enlarge the inherent social circle. However, the flourish of MSNs also hinges upon fully understanding and managing the challenges, such as security threats and privacy leakage. Security and privacy concerns rise as the boom of MSN applications comes up, but few users have paid adequate attentions to protect their privacy-sensitive information from disclosing. First of all, to initiate social interactions, users sometimes exchange their social interests or preferences with each other (including strangers in the vicinity) without sufficient protections. As such, some private information may be inferred from the exchanged social interests by attackers and untrusted users. Secondly, some malicious attackers might forge fake identities or false contents, such as spam and advertisements, to disrupt MSNs or mislead other users. These attackers could even collude and launch a series of security threats to MSNs. In addition, massive social network data are usually stored in untrusted cloud servers, where data confidentiality, authentication, access control and privacy are of paramount importance. Last but not least, the trade-off between data availability and privacy should be taken into account when the data are stored, queried and processed for various MSN applications. Therefore, novel security and privacy techniques become essential for MSN to provide sufficient and adjustable protections. In this thesis, we focus on security and privacy for MSNs. Based on the MSN architecture and emerging applications, we first investigate security and privacy requirements for MSNs and introduce several challenging issues, i.e., spam, misbehaviors and privacy leakage. To tackle these problems, we propose efficient security and privacy preservation schemes for MSNs. Specifically, the main contributions of this thesis can be three-fold. Firstly, to address the issues of spam in autonomous MSNs, we propose a personalized fine-grained spam filtering scheme (PIF), which exploits social characteristics during data delivery. The PIF allows users to create personalized filters according to their social interests, and enables social friends to hold these filters, discarding the unwanted data before delivery. We also design privacy-preserving coarse-grained and fine-grained filtering mechanisms in the PIF to not only enable the filtering but also prevent users' private information included in the filters from disclosing to untrusted entities. Secondly, to detect misbehaviors during MSN data sharing, we propose a social-based mobile Sybil detection scheme (SMSD). The SMSD detects Sybil attackers by differentiating the abnormal pseudonym changing and contact behaviors, since Sybil attackers frequently or rapidly change their pseudonyms to cheat legitimate users. As the volume of contact data from users keeps increasing, the SMSD utilizes local cloud servers to store and process the users' contact data such that the burden of mobile users is alleviated. The SMSD also detects the collusion attacks and prevents user's data from malicious modification when employing the untrusted local cloud server for the detection. Thirdly, to achieve the trade-off between privacy and data availability, we investigate a centralized social network application, which exploits social network to enhance human-to-human infection analysis. We integrate social network data and health data to jointly analyze the instantaneous infectivity during human-to-human contact, and propose a novel privacy-preserving infection analysis approach (PIA). The PIA enables the collaboration among different cloud servers (i.e., social network cloud server and health cloud server). It employs a privacy-preserving data query method based on conditional oblivious transfer to enable data sharing and prevent data from disclosing to untrusted entities. A privacy-preserving classification-based infection analysis method is also proposed to enable the health cloud server to infer infection spread but preserve privacy simultaneously. Finally, we summarize the thesis and share several open research directions in MSNs. The developed security solutions and research results in this thesis should provide a useful step towards better understanding and implementing secure and privacy-preserving MSNs

Secure steganography, compression and diagnoses of electrocardiograms in wireless body sensor networks

Submission of this completed form results in your thesis/project being lodged online at the RMIT Research Repository. Further information about the RMIT Research Repository is available at http://researchbank.rmit.edu.au Please complete abstract and keywords below for cataloguing and indexing your thesis/project. Abstract (Minimum 200 words, maximum 500 words) The usage of e-health applications is increasing in the modern era. Remote cardiac patients monitoring application is an important example of these e-health applications. Diagnosing cardiac disease in time is of crucial importance to save many patients lives. More than 3.5 million Australians suffer from long-term cardiac diseases. Therefore, in an ideal situation, a continuous cardiac monitoring system should be provided for this large number of patients. However, health-care providers lack the technology required to achieve this objective. Cloud services can be utilized to fill the technology gap for health-care providers. However, three main problems prevent health-care providers from using cloud services. Privacy, performance and accuracy of diagnoses. In this thesis we are addressing these three problems. To provide strong privacy protection services, two steganography techniques are proposed. Both techniques could achieve promising results in terms of security and distortion measurement. The differences between original and resultant watermarked ECG signals were less then 1%. Accordingly, the resultant ECG signal can be still used for diagnoses purposes, and only authorized persons who have the required security information, can extract the hidden secret data in the ECG signal. Consequently, to solve the performance problem of storing huge amount of data concerning ECG into the cloud, two types of compression techniques are introduced: Fractal based lossy compression technique and Gaussian based lossless compression technique. This thesis proves that, fractal models can be efficiently used in ECG lossy compression. Moreover, the proposed fractal technique is a multi-processing ready technique that is suitable to be implemented inside a cloud to make use of its multi processing capability. A high compression ratio could be achieved with low distortion effects. The Gaussian lossless compression technique is proposed to provide a high compression ratio. Moreover, because the compressed files are stored in the cloud, its services should be able to provide automatic diagnosis capability. Therefore, cloud services should be able to diagnose compressed ECG files without undergoing a decompression stage to reduce additional processing overhead. Accordingly, the proposed Gaussian compression provides the ability to diagnose the resultant compressed file. Subsequently, to make use of this homomorphic feature of the proposed Gaussian compression algorithm, in this thesis we have introduced a new diagnoses technique that can be used to detect life-threatening cardiac diseases such as Ventricular Tachycardia and Ventricular Fibrillation. The proposed technique is applied directly to the compressed ECG files without going through the decompression stage. The proposed technique could achieve high accuracy results near to 100% for detecting Ventricular Arrhythmia and 96% for detecting Left Bundle Branch Block. Finally, we believe that in this thesis, the first steps towards encouraging health-care providers to use cloud services have been taken. However, this journey is still long