Wearable sensor systems composed of small and light sensing nodes have the potential to revolutionise healthcare. While uptake has increased over time in a variety of application areas, it has been slowed by problems such as lack of infrastructure and the functional capabilities of the systems themselves. An important application of wearable sensors is the detection of falls, particularly for elderly or otherwise vulnerable people. However, existing solutions do not provide the detection accuracy required for the technology to gain the trust of medical professionals. This thesis aims to improve the state of the art in automated human fall detection algorithms through the use of a machine learning based algorithm combined with novel data annotation and feature extraction methods. Most wearable fall detection algorithms are based on thresholds set by observational analysis for various fall types. However, such algorithms do not generalise well for unseen datasets. This has thus led to many fall detection systems with claims of high performance but with high rates of False Positive and False Negative when evaluated on unseen datasets. A more appropriate approach, as proposed in this thesis, is a machine learning based algorithm for fall detection. The work in this thesis uses a C4.5 Decision Tree algorithm and computes input features based on three fall stages: pre-impact, impact and post-impact. By computing features based on these three fall stages, the fall detection algorithm can learn patterns unique to falls. In total, thirteen features were selected across the three fall stages out of an original set of twenty-eight features. Further to the identification of fall stages and selection of appropriate features, an annotation technique named micro-annotation is proposed that resolves annotation-related ambiguities in the evaluation of fall detection algorithms. Further analysis on factors that can impact the performance of a machine learning based algorithm were investigated. The analysis defines a design space which serves as a guideline for a machine learning based fall detection algorithm. The factors investigated include sampling frequency, the number of subjects used for training, and sensor location. The optimal values were found to be10Hz, 10 training subjects, and a single sensor mounted on the chest. Protocols for falls and Activities of Daily Living (ADL) were designed such that the developed algorithms are able to cope under a variety of real world activities and events. A total of 50 subjects were recruited to participate in the data gathering exercise. Four common types of falls in the sagittal and coronal planes were simulated by the volunteers; and falls in the sagittal plane were additionally induced by applying a lateral force to blindfolded volunteers. The algorithm was evaluated based on leave one subject out cross validation in order to determine its ability to generalise to unseen subjects. The current state of the art in the literature shows fall detectors with an F-measure below 90%. The commercial Tynetec fall detector provided an F-measure of only 50% when evaluated here. Overall, the fall detection algorithm using the proposed micro-annotation technique and fall stage features provides an F-measure of 93% at 10Hz, exceeding the performance provided by the current state of the art.

Today one of the fast growing social services is the ability for doctors to monitor patients in their residences. The proposed highly scalable database system is designed to support a Remote Patient Monitoring system (RPMS). In an RPMS, a wide range of applications are enabled by collecting health related measurement results from a number of medical devices in the patient’s home, parsing and formatting these results, and transmitting them from the patient’s home to specific data stores. Subsequently, another set of applications will communicate with these data stores to provide clinicians with the ability to observe, examine, and analyze these health related measurements in (near) real-time. Because of the rapid expansion in the number of patients utilizing RPMS, it is becoming a challenge to store, manage, and process the very large number of health related measurements that are being collected. The primary reason for this problem is that most RPMSs are built on top of traditional relational databases, which are inefficient when dealing with this very large amount of data (often called “big data”). This thesis project analyzes scalable data management to support RPMSs, introduces a new set of open-source technologies that efficiently store and manage any amount of data which might be used in conjunction with such a scalable RPMS based upon HBase, implements these technologies, and as a proof of concept, compares the prototype data management system with the performance of a traditional relational database (specifically MySQL). This comparison considers both a single node and a multi node cluster. The comparison evaluates several critical parameters, including performance, scalability, and load balancing (in the case of multiple nodes). The amount of data used for testing input/output (read/write) and data statistics performance is 1, 10, 50, 100, and 250 GB. The thesis presents several ways of dealing with large amounts of data and develops & evaluates a highly scalable database that could be used with a RPMS. Several software suites were used to compare both relational and non-relational systems and these results are used to evaluate the performance of the prototype of the proposed RPMS. The results of benchmarking show that MySQL is better than HBase in terms of read performance, while HBase is better in terms of write performance. Which of these types of databases should be used to implement a RPMS is a function of the expected ratio of reads and writes. Learning this ratio should be the subject of a future thesis project. / En av de snabbast växande sociala tjänsterna idag är möjligheten för läkare att övervaka patienter i sina bostäder. Det beskrivna, mycket skalbara databassystemet är utformat för att stödja ett sådant Remote Patient Monitoring-system (RPMS). I ett RPMS kan flertalet applikationer användas med hälsorelaterade mätresultat från medicintekniska produkter i patientens hem, för att analysera och formatera resultat, samt överföra dem från patientens hem till specifika datalager. Därefter kommer ytterligare en uppsättning program kommunicera med dessa datalager för att ge kliniker möjlighet att observera, undersöka och analysera dessa hälsorelaterade mått i (nära) realtid. På grund av den snabba expansionen av antalet patienter som använder RPMS, är det en utmaning att hantera och bearbeta den stora mängd hälsorelaterade mätningar som samlas in. Den främsta anledningen till detta problem är att de flesta RPMS är inbyggda i traditionella relationsdatabaser, som är ineffektiva när det handlar om väldigt stora mängder data (ofta kallat "big data"). Detta examensarbete analyserar skalbar datahantering för RPMS, och inför en ny uppsättning av teknologier baserade på öppen källkod som effektivt lagrar och hanterar godtyckligt stora datamängder. Dessa tekniker används i en prototypversion (proof of concept) av ett skalbart RPMS baserat på HBase. Implementationen av det designade systemet jämförs mot ett RPMS baserat på en traditionell relationsdatabas (i detta fall MySQL). Denna jämförelse ges för både en ensam nod och flera noder. Jämförelsen utvärderar flera kritiska parametrar, inklusive prestanda, skalbarhet, och lastbalansering (i fallet med flera noder). Datamängderna som används för att testa läsning/skrivning och statistisk prestanda är 1, 10, 50, 100 respektive 250 GB. Avhandlingen presenterar flera sätt att hantera stora mängder data och utvecklar samt utvärderar en mycket skalbar databas, som är lämplig för användning i RPMS. Flera mjukvaror för att jämföra relationella och icke-relationella system används för att utvärdera prototypen av de föreslagna RPMS och dess resultat. Resultaten av dessa jämförelser visar att MySQL presterar bättre än HBase när det gäller läsprestanda, medan HBase har bättre prestanda vid skrivning. Vilken typ av databas som bör väljas vid en RMPS-implementation beror därför på den förväntade kvoten mellan läsningar och skrivningar. Detta förhållande är ett lämpligt ämne för ett framtida examensarbete.

Medical literature identifies a number of technology-driven improvements in disease management such as implantable medical devices (IMDs) that are a standard treatment for candidates with specific diseases. Among patients using implantable cardiac defibrillators (ICD), for example, problems and issues are being discovered faster compared to patients without monitoring, improving safety. What is not known is why patients report not feeling safer, creating a safety paradox, and why patients identify privacy concerns in ICD monitoring. There is a major gap in the literature regarding the factors that contribute to perceived safety and privacy in remote patient monitoring (RPM). To address this gap, the research goal of this study was to provide an interpretive account of the experience of RPM patients. This study investigated two research questions: 1) How did RPM recipients perceive safety concerns?, and 2) How did RPM recipients perceive privacy concerns? To address the research questions, in-depth, semi-structured interviews were conducted with six participants to explore individual perceptions in rich detail using interpretative phenomenological analysis (IPA). Four themes were identified and described based on the analysis of the interviews that include — comfort with perceived risk, control over information, education, and security — emerged from the iterative review and data analysis. Participants expressed comfort with perceived risk, however being scared and anxious were recurrent subordinate themes. The majority of participants expressed negative feelings as a result of an initial traumatic event related to their devices and lived in fear of being shocked in inopportune moments. Most of these concerns stem from lack of information and inadequate education. Uncertainties concerning treatment tends to be common, due to lack of feedback from ICD RPM status. Those who knew others with ICD RPM became worrisome after hearing about incidences of sudden cardiac death (SCD) when the device either failed or did not work adequately to save their friend’s life. Participants also expressed cybersecurity concerns that their ICD might be hacked, maladjusted, manipulated with magnets, or turned off. They believed ICD RPM security was in place but inadequate as well as reported feeling a lack of control over information. Participants expressed wanting the right to be left alone and in most cases wanted to limit others’ access to their information, which in turn, created conflict within families and loved ones. Geolocation was a contentious node in this study, with most of participants reporting they did not want to be tracked under any circumstances. This research was needed because few researchers have explored how people live and interact with these newer and more advanced devices. These findings have implications for practice relating to RPM safety and privacy such as identifying a gap between device companies, practitioners, and participants and provided directions for future research to discover better ways to live with ICD RPM and ICD shock.

The intent of this project was to integrate an existing mobile phone-based telemonitoring system into a home care nursing environment. Analyses were conducted to examine nursing workflows and home care constraints. User-centric design, development, and testing were used to modify the current telemonitoring system for a home care pilot study with heart failure clients. Interim results show technology acceptance by home care nurses and improved self-awareness in clients; the telehomecare system offers its greatest value as an opportunity for client education following clinical alerts. The pilot study will continue with a focus on increased client recruitment and selectivity towards those most in need of chronic disease management. It is expected that the system will result in an improvement in health outcomes and more efficient delivery of home care visits. The results from this study will provide insight into the impact of a new service delivery model for home care nurses.

The intent of this project was to integrate an existing mobile phone-based telemonitoring system into a home care nursing environment. Analyses were conducted to examine nursing workflows and home care constraints. User-centric design, development, and testing were used to modify the current telemonitoring system for a home care pilot study with heart failure clients. Interim results show technology acceptance by home care nurses and improved self-awareness in clients; the telehomecare system offers its greatest value as an opportunity for client education following clinical alerts. The pilot study will continue with a focus on increased client recruitment and selectivity towards those most in need of chronic disease management. It is expected that the system will result in an improvement in health outcomes and more efficient delivery of home care visits. The results from this study will provide insight into the impact of a new service delivery model for home care nurses.

Summary: Although the digital revolution has transformed many niches of human activity, healthcare sector and pharmaceutical drug development has been relatively slow in embracing emerging technologies to optimize health efficacy, especially in Nordic Countries. The topic is of more importance now owing to the present scenario of the corona virus (COVID-19)outbreak, which has caused unparalleled disruption in the conduct of clinical trials and presented challenges as well as opportunities for clinical trialists and data analysts. In this master thesis, the potential opportunity with virtual or digital clinical trials as viable options to enhance drug development efficiency is highlighted that offers diverse patients easier and attractive ways to participate in clinical trials. Special reference is made to wearable devices in clinical trial execution and generating real world data; its acquisition and processing in a virtual trial setting. Issues of patient safety, measurement reliability and validity, and data privacy & integrity are  reviewed, and considerations are put forward for mitigation of underlying regulatory andoperational barriers. The aim of this thesis is to assess the recent wearable technologies that generate Real World Data and to understand the potential of this data to transform Nordic healthcare industry. A systematic review of clinical trials involving wearable patient monitoring technique in North America, Nordic Countries and other European countries was conducted. Out of various innovative wearable technologies, Smartwatches are found to be the most common and it is also observed that these wearable technologies have been able to help in early detection and diagnosis of diseases and modify disease progression by real time monitoring of data and develop precision medicine. thus, it is concluded that Wearable Remote Patient Monitoring is a novel technique that has few barriers;but promises a big transformation in Nordic Countries as well as in entire healthcare industry.

No description available.

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-05-04T17:30:47Z No. of bitstreams: 1 ALDENOR FALCÃO MARTINS – DISSERTAÇÃO (PPGEE) 2015.pdf: 15602466 bytes, checksum: 608173ca67ff68da8ae45b321aa82204 (MD5) / Made available in DSpace on 2018-05-04T17:30:47Z (GMT). No. of bitstreams: 1 ALDENOR FALCÃO MARTINS – DISSERTAÇÃO (PPGEE) 2015.pdf: 15602466 bytes, checksum: 608173ca67ff68da8ae45b321aa82204 (MD5) Previous issue date: 2015-04-24 / Segundo o IBGE em 2013, o Brasil apresentava 13% de sua população composta por pessoas acima de 65 anos, somado a isto, o estilo de vida das sociedades ocidentais tem facilitado o aparecimento de doenças crônicas cada vez mais cedo. A premissa é que tornemos mais eficiente a utilização do nosso sistema de saúde, pois este é um recurso escasso. Uma forma de melhorar esta eficiência é assegurar que os tratamentos prescritos serão devidamente seguidos. Quando o paciente se encontra no hospital uma gama de recursos monitora a saúde do paciente oferecendo acompanhamento seguro na eventualidade de um desvio, alertando e armazenando as informações do paciente no decorrer de suas atividades. Um recurso que ajuda no acompanhamento deste paciente é a monitoração remota do paciente, que possibilita que sensores enviem a informação da condição de saúde do paciente e permitam o acompanhamento do mesmo. Sistemas Físicos Cibernéticos (SFC) são entidades computacionais ligadas em rede que operam entidades no mundo físico de maneira cooperativa. Tais sistemas podem ser utilizados em redes de monitoramento remoto de pacientes com o fim de apresentar e ajustar o tratamento de acordo com as recomendações do médico. Este trabalho propõe um passo na direção da autonomia, que permita uma melhor qualidade de vida ao paciente crônico, permitindo que situações conhecidas e dentro de um regime de segurança previamente determinado pelo médico sejam ajustadas. Este trabalho apresenta uma proposta de um Sistema Físico Cibernético (SFC), que permite que adequações ao tratamento previamente elaboradas sejam colocadas em planos de tratamento por meio de agentes inteligentes e de planejadores SAT e sejam disponibilizadas de acordo com a mudança da condição do paciente, através de uma rede monitoramento do paciente, seguindo padrões estabelecidos para dispositivos médicos utilizados em casa que disponibiliza o tratamento ao paciente. O modelo proposto é indicado para o acompanhamento em casa de doenças crônicas através de um coletor central responsável pela coordenação do acompanhamento do paciente. / According to IBGE in 2013 13% of the population had 60 or more years old. As the national population ages, we have to move towards more efficient use of SUS. A way to improve is the closer followup of patient’s evolution by the healthcare professional. At the hospital the patient has access to a set of equipments and expert knowledge capable to correct the treatment path. From this scenario it is easy to imply the need for a change, the current status quo is unbearable financially and cumbersome for patient and doctor routines. A resource that helps is the remote patient monitoring (RPM) , where sensors provide the latest information about patient’s health status and are able to suggest a course correction on the treatment path. A Cyber-Physical System (CPS) is a network of interacting computational entities with physical inputs and outputs that work together towards a goal. A CPS can be part of a RPM in order to present and adjust the treatment according to the healthcare professional recommendations. This work offers a framework for situations where the medical expert knowledge is complete allowing changes on the treatment path be adjusted with minimum risk. Our proposal to deal with the problem is a CPS based remote patient monitoring network where a model for the system is developed based on Multiagent Agent System (MAS) and automatic planning system based on SAT, allowing safe and minimal course correction on treatment paths already set for a patient. This proposal operates through a central hub element responsible to coordinate the followup of the patient.

Submitted by Ana Caroline Costa (ana_caroline212@hotmail.com) on 2018-11-30T19:09:50Z No. of bitstreams: 2 Dissertação - Hugo de Almeida Ribeiro - 2018.pdf: 2177519 bytes, checksum: fb8741d8762fdd3e58e586442025299e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-12-03T13:16:23Z (GMT) No. of bitstreams: 2 Dissertação - Hugo de Almeida Ribeiro - 2018.pdf: 2177519 bytes, checksum: fb8741d8762fdd3e58e586442025299e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-12-03T13:16:23Z (GMT). No. of bitstreams: 2 Dissertação - Hugo de Almeida Ribeiro - 2018.pdf: 2177519 bytes, checksum: fb8741d8762fdd3e58e586442025299e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-11-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Healthcare demand is expected to surpass healthcare offer in the next years, encumbering the healthcare system. In one side we have an increase in life expectancy, and therefore the occurrence of chronic diseases, while on the other side we have a shortage of healthcare professionals. Remote patient monitoring systems can be used to continuously keep track of a person health status. When integrated to social networking services, we can build a carer network around the patient. This network is composed of formal (healthcare professionals) and informal carers (family and friends). Information regarding the patient health status can be disseminated within the carer network. Then the remote patient monitoring system may instruct informal carers to assist the patient before recommending formal care. Thus reducing the demands over the healthcare system. There are research initiatives on health information dissemination using social networking. But these initiatives are isolated. This dissertation aims to propose a domain model and an architectural model for remote patient monitoring systems that integrate social networking services unifying concepts found in state-of-the-art literature. The proposed solution is based on three sub-domains: remote patient monitoring systems, enable continuous health status monitoring; social networking services, allow actors to show interest in monitoring other actors health status; and event notification systems, distribute health information among actors. An skeletal system was built to validate the proposal. This skeletal systems consists on modifying an existing system to satisfy restrictions imposed by the proposed models and an usage scenario. / Espera-se que nos próximos anos a demanda por cuidados de saúde supere a oferta, debilitando o sistema de saúde. Os aumentos da expectativa de vida e da ocorrência de doenças crônicas vão de encontro a falta de profissionais de saúde. Sistemas de monitoramento remoto de pacientes podem ser usados para acompanhar continuamente o estado de saúde de uma pessoa. Quando integrados a serviços de redes sociais, uma rede de cuidadores pode ser formada ao redor do paciente. Esta rede é composta por cuidadores formais (profissionais de saúde) e informais (familiares e amigos). Informação a respeito do estado de saúde do paciente pode ser disseminada na rede de cuidadores. Dessa forma, o sistema de monitoramento remoto pode instruir cuidadores informais a auxiliar o paciente antes de recomendar cuidado profissional, reduzindo, portanto, as demandas sobre o sistema de saúde. Existem esforços de pesquisa de soluções que permitam a disseminação de informação de saúde usando abordagem de redes sociais. No entanto, essas pesquisas encontram-se isoladas. Este trabalho tem como objetivo propor um modelo de domínio e um modelo arquitetural que integrem serviços de rede social para disseminar informação de um paciente para sua rede de cuidadores, unificando os conceitos presentes na literatura. A solução apresentada baseia-se em três subdomínios: sistemas de monitoramento remoto de pacientes, os quais permitem o acompanhamento contínuo do estado de saúde dos pacientes; serviços de redes sociais, que possibilitam que os atores do sistema informem ter interesse em acompanhar o estado de saúde de outros atores; e sistemas de notificação de eventos, que distribuem as informações de saúde entre os atores. A validação foi realizada por meio de esqueleto de sistema, consistindo na modificação de um sistema existente, satisfazendo às restrições dos modelos propostos e atendendo a um cenário de uso.

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each algorithm for monitoring CVD is also discussed. Both models were able to classify CVD risk with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, unlike current systems the resource requirements for each component in the system was evaluated. The MLP was found to be more efficient when running on the mobile device compared to the SVM. The results of thesis also show that the MLAs complexity was not a barrier to deployment on a mobile device. / Thesis / Master of Applied Science (MASc) / In this thesis, a novel mobile system for monitoring cardiovascular (CVD) disease is presented. The system allows for the continuous monitoring of both physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a remote server. A comparative analysis between the support vector machine (SVM) and multilayer perceptron (MLP) to explore the effectiveness of each MLA for monitoring CVD is also discussed. Both models were able to classify CVD severity with the SVM achieving the highest accuracy (63%) and specificity (76%). Finally, the resource requirements for each component in the system were evaluated. The results show that the MLAs complexity was not a barrier to deployment on a mobile device.