175 research outputs found

    Mobile Health Technologies

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    Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

    Ubiquitous Computing

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    The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

    Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

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    Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

    Technology and Australia's Future: New technologies and their role in Australia's security, cultural, democratic, social and economic systems

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    Chapter 1. Introducing technology -- Chapter 2. The shaping of technology -- Chapter 3. Prediction of future technologies -- Chapter 4. The impacts of technology -- Chapter 5. Meanings, attitudes and behaviour -- Chapter 6. Evaluation -- Chapter 7. Intervention -- Conclusion - adapt or wither.This report was commisioned by Australian Council of Learned Academies

    The case for investment in technology to manage the global costs of dementia

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    Worldwide growth in the number of people living with dementia will continue over the coming decades and is already putting pressure on health and care systems, both formal and informal, and on costs, both public and private. One response could be to make greater use of digital and other technologies to try to improve outcomes and contain costs. We were commissioned to examine the economic case for accelerated investment in technology that could, over time, deliver savings on the overall cost of care for people with dementia. Our short study included a rapid review of international evidence on effectiveness and cost-effectiveness of technology, consideration of the conditions for its successful adoption, and liaison with people from industry, government, academic, third sector and other sectors, and people with dementia and carers. We used modelling analyses to examine the economic case, using the UK as context. We then discussed the roles that state investment or action could play, perhaps to accelerate use of technology so as to deliver both wellbeing and economic benefits

    Language learning and technology

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    By and large, languages, both as first, second or foreign languages remain one of the most important core subjects at every educational level. In early stages, their inclusion in the curriculum is intricately connected with (pre-)literacy practices, but also as a main driver for the successful integration of minority students learning a second language. In addition, the attainment of a certain level of a foreign language by the end of compulsory education is a common goal in most educational systems around the globe. Arguably, the key drivers of success in learning a language range from motivational to attitudinal, but ultimately they also have to do with the amount of target language use, the access to quality input, and especially language teachers' readiness to incorporate the latest educational trends effectively in the language classroom, educational technologies amongst them

    Novel sedentary behaviour measurement methods: application for self-monitoring in adults

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    With the introduction of the technological age, increasing mechanisation has led to labour saving devices which have all-but engineered physical activity out of our lives and sedentary behaviour has now become the default behaviour during waking hours. Interventions that previously focused on improving levels of physical activity are now attempting to concurrently increase levels of physical activity and decrease time spent in sedentary behaviour. One method that has shown promise in interventions to increase physical activity and healthy eating in adults is the behaviour change technique of self-monitoring. There is now a robust set of literature indicating self-monitoring as the most promising behaviour change technique in this area. Self-monitoring is tied inherently into the recent rise in wearable technology. These new devices have the ability to track a variety of behavioural and physiological parameters and immediately make the information returnable to the user via connected mobile applications. The potential pervasive nature of these technologies and their use of robust behaviour change techniques could make them a useful tool in interventions to reduce sedentary behaviour. Therefore the overall purpose of this three study dissertation was to identify and validate technology that can self-monitor sedentary behaviour and to determine its feasibility in reducing sedentary behaviour. Study 1 Purpose: The aim of this study was to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary behaviour and/or physical activity. Methods: To identify technologies, four scientific databases were systematically searched using key terms related to behaviour, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1st, 2015. Results: The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored physical activity, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring physical activity and/or sedentary behaviour; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on physical activity (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary behaviour (activPAL VT, the LumoBack, and Darma). Conclusions: There are a large number of devices that self-monitor physical activity; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behaviour change interventions, although the growing field of wearable technology may facilitate this to change. Study 2 Purpose: The aim of this study was to examine the criterion and convergent validity of the LumoBack as a measure of sedentary behaviour compared to direct observation, the ActiGraph wGT3X+ and the activPAL under laboratory and free-living conditions in a sample of healthy adults. Methods: In the laboratory experiment, 34 participants wore a LumoBack, ActiGraph and activPAL monitor and were put through seven different sitting conditions. In the free-living experiment, a sub-sample of 12 participants wore the LumoBack, ActiGraph and activPAL monitor for seven days. Validity were assessed using Bland-Altman plots, mean absolute percentage error (MAPE), and intraclass correlation coefficient (ICC). T-test and Repeated Measures Analysis of Variance were also used to determine any significant difference in measured behaviours. Results: In the laboratory setting, the LumoBack had a mean bias of 76.2, 72.1 and -92.3 seconds when compared to direct observation, ActiGraph and activPAL, respectively, whilst MAPE was less than 4%. Furthermore, the ICC was 0.82 compared to the ActiGraph and 0.73 compared to the activPAL. In the free-living experiment, mean bias was -4.64, 8.90 and 2.34 seconds when compared to the activPAL for sedentary behaviour, standing time and stepping time respectively. Mean bias was -38.44 minutes when compared to the ActiGraph for sedentary time. MAPE for all behaviours were 0.75. Conclusion: The LumoBack has acceptable validity and reliability as a measure of sedentary behaviour. Study 3 Purpose: The aim of this study was to explore the use of the LumoBack as a behaviour change tool to reduce sedentary behaviour in adults. Methods: Forty-two participants (≥25 years) who had an iPhone 4S or later model wore the LumoBack without any feedback for one week for baseline measures of behaviour. Participants then wore the LumoBack for a further five weeks whilst receiving feedback on sedentary behaviour via a sedentary vibration from the device and feedback on the mobile application. Sedentary behaviour, standing time, and stepping time were objectively assessed using the LumoBack. Differences in behaviour were determined between baseline, week 1 and week 5. Participant engagement with the LumoBack was determined using Mobile app analytics software. Results: There were no statistically significant differences in behaviour between baseline and the LumoBack intervention period (p>0.05). Participants engaged most with the Steps card on the LumoBack app with peaks in engagement seen at week 5. Conclusion: This study indicates that using the LumoBack on its own was not effective in reducing sedentary behaviour in adults. Self-monitoring and feedback may need to be combined with other behaviour change strategies such as environmental restructuring to be effective. General Conclusion This thesis found that there are currently an abundance of technologies which self-monitors physical activity but a lack of devices which measuring sedentary behaviour. One such device, the LumoBack, has shown to have acceptable validity as a measure of sedentary behaviour. Whilst the use of the LumoBack as a behaviour change tool did not elicit any significant changes, its ability to be a pervasive behavioural intervention and the use of user-defined nudging can make the LumoBack, and other similar low cost, valid objective sedentary behaviour self-monitors key components in multi-faceted interventions

    Pervasive Media and Eudaimonia: Transdisciplinary Research by Practice

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    As mobile technologies and prolific digital media saturate and intrude upon daily reality for many people, this research practice provides an alternative pathway in which creative engagement with pervasive media offers a holistic experience of oneself in relation to the people, place and technologies of our time. This thesis introduces the concept of eudaimonia as creative well-being, in relation to pervasive media. The dual meaning of eudaimonia as an individual’s own right path of flourishing and as the good-daimon, muse or guardian who guides and inspires the action of walking such a path, highlights the tensions implicit in the work. Tensions that embrace user and author, inside and outside, urban and rural, movement and stillness – until a common ground of symmathesy occurs. Taking a transdisciplinary approach to this phenomenological enquiry, the work of community arts facilitation is brought into dialogue with Grove’s Clean toolkit, originally developed in the field of clinical psychology. The thesis is presented as a phenomenological text with online creative portfolio and appendices. Other artists’ works are described subjectively as part of the practice-based method. Research findings are presented in relation to themes of Space, Presence, Community and Iteration from which emerge the framework of creative practice and the researcher’s conceptual model of Anthroposensory Sculpture. Four public art projects were delivered with diverse communities, landscapes and foci of attention, from which a framework of creative practice is revealed that supports eudaimonic engagement with personal and collective, metaphoric and geographic landscape: Soundlines (2009-10, North Somerset, UK), Experimental Walks (2010-14, UK and Canada), Hunter Gatherer (2010-11, Yorkshire Dales, UK), Living Voices (2011-13 Wiltshire, UK). Through the Experimental Walks project, a Colour Grid methodology developed, that invites sensory noticing and notation, subsequently produced as iPhone app Hunter Gatherer (2011). This research which will be of value to researchers and practitioners seeking to understand engagement of people with place, media and technology. Pioneering in its use of Clean as an arts methodology, this research adds to a growing interest in Clean methodology for research. The thesis contributes to ongoing debates about how to build a more caring society in which each individual can flourish; as such it will be of interest to others exploring the multiple dimensions of well-being and the use of emergent platforms for digital media and art

    Intel Galileo and Intel Galileo Gen 2

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    Computer scienc

    Responsible innovation in mobile journalism : Exploring professional journalists` learning and innovation processes

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    Denne avhandlingen handler om innovasjon i mobiljournalistikk, og utforsker hvordan profesjonelle TV- og avisjournalister bruker smarttelefoner som journalistisk produksjonsverktøy. I tillegg reflekteres kritisk over utfordringer som kan knyttes til at journalister satser i sitt arbeid på datateknologi som ikke bare integrerer flere risikoteknologier men bygger på infrastrukturer som er optimalisert for omfattende dataekstraksjon og kommersielle overvåkingspraksiser. Det overordnete spørsmålet som søkes besvart i avhandlingen er: Hva er ansvarlig innovasjon i mobiljournalistikk? For å finne svar på forskningsspørsmålet kombineres empiriske tilnærminger og analytisk-teoretiske perspektiver. Innovasjon forstås her som en kompleks sosiokulturell læringsprosess der ´ansvarlig innovasjon´ pekes ut som en normativ meta-kategori. I den empiriske delen i avhandlingen undersøkes profesjonelle journalisters konkrete lærings- og innovasjonsprosesser. Basert på etnografi-inspirerte metoder som deltakende observasjon, dybdeintervjuer og uformelle samtaler belyser den empiriske delen av avhandlingen innovasjon i mobiljournalistikk gjennom to ulike casestudier. I den første casen utforskes et globalt pioner-nettverk som fremstår som en viktig kollektiv aktør i innovativ mobiljournalistikk. I den andre casen undersøkes et konkret trainingsarrangement for profesjonelle avisjournalister som ledd i en omfattende innovasjonsprosess i en tradisjonell medieorganisasjon. Den analytisk-teoretiske delen av avhandlingen tar for seg meta-konseptet `ansvarlig innovasjon´ og belyser kritisk den politiske økonomien knyttet til lærings- og kunnskapsutvikling. Ved hjelp av Zuboffs (2019) teori om overvåkningskapitalisme fokuserer denne delen av avhandlingen på større og mer langsiktige samfunnskonsekvenser knyttet til bruk av mobilteknologi i journalistikk. Ved å peke på ulike risikoer ved uregulerte former for datainnsamling og utfordringer knyttet til privatisering av kunnskap og kunnskapsproduksjon omhandler den teoretisk-analytiske delen hva som står på spill for journalister, medieorganisasjoner og samfunnet i sin helhet når mobilteknologi blir tatt ukritisk i bruk. Det konkluderes med at en uansvarlig og risikofylt bruk av mobilteknologi og relaterte infrastrukturer ikke tegner et bilde av mobiljournalistikk som en demokratiserende kraft (og tidsriktig produksjonsmåte) men heller en praksis som kan bidra til å undergrave demokratiets fundamenter gjennom omfattende dataekstraksjon og kommersielt motiverte overvåkningspraksiser. For å møte komplekse risikoer ved bruk av teknologisk innovasjon i mobiljournalistikk og å kunne finne konstruktive løsninger diskuteres det nye europeiske forsknings- og innovasjonsrammeverket Responsible Research and Innovation (RRI) som sikter mot grunnleggende endringer i nåværende innovasjons- og forskningspraksis. Med utgangspunkt i idéer og metoder fra RRI foreslås ulike handlingsopsjoner på individ-, organisasjonps- og samfunnsnivå samt anbefalinger hva `ansvarlig innovasjon i mobiljournalistikk` innebærer. Et overordnet mål med avhandlingen er å bidra i, og berike, den akademiske og offentlige debatten ved å gi konkrete innblikk i profesjonelle journalisters læringssituasjoner og innovasjonsprosesser og gjennom den rette oppmerksomheten mot fundamentale utfordringer ved bruk av kompleks datateknologi og infrastrukturer i samfunnet.This thesis examines innovation in the field of mobile journalism by examining how professional broadcast and print journalists learn about and adopt mobile technology for their journalistic practice and by investigating critically the side effects from journalists’ adoption of mobile computing platforms, encompassing highly convergent and different risk technologies. The overarching research question that guided this work asked: What is responsible innovation in mobile journalism? To find answers to this overarching research endeavor, I applied an approach that combines empirical and analytical-conceptual perspectives. Innovation is conceptualized in this work as a complex sociocultural process of learning, and responsible innovation is viewed as a meta-category of innovation. The empirical part sets out to understand actual learning practices and innovation processes by examining how professional print and broadcast journalists learn to adopt mobile technology and innovate through mobile journalism in different social settings. Based on a qualitative approach that applies methods such as long-term observations, participant observation, in-depth interviews, and informal conversations, the empirical part of the thesis provides insight into professional journalists’ individual motivations and experiences, organizational and new collective approaches to innovation, and learning processes. The conceptual part of the thesis examines the meta-concept of “responsible innovation” more closely by applying a critical perspective of political economy on learning and knowledge processes. Viewed through the lens of Zuboff’s (2019) surveillance capitalism theory, this part of the thesis draws attention to broader societal consequences attached to the adoption of mobile technology in journalism. By uncovering emerging risks and challenges from unregulated dataveillance and privatization of knowledge, this part demonstrates what is at stake if mobile technology is irresponsibly adopted by a risk group – in this case, journalists – and how, from this perspective, mobile journalism fails to emerge as a democratic force, thereby undermining the fundaments of democracy. To counteract the identified and complex risks from comprehensive data extraction and dataveillance that accompany journalists and media organizations’ adoption of and innovation in mobile journalism, ideas and methods from the European Union’s Responsible Research and Innovation framework are suggested as a possible approach. This is specified by outlining different implications from the identified risks on individual, organizational, and societal levels, and by making suggestions as to what “responsible innovation” in mobile journalism would encompass in the context of this thesis. This thesis aims to build on existing academic discussions through enriching debates in the mobile journalism field by providing insights into professional journalists’ concrete learning and innovation processes, as well as directing attention toward individual, organizational, and societal risks attached to uncritical adoption of a complex and pervasive computing platform in journalism practice and innovation in the field.Doktorgradsavhandlin
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