1,079 research outputs found
SUMP for Cities’ Sustainable Development
In a rapidly changing world, it is necessary to increase the engagement of local authorities and stakeholders to make urban mobility cleaner and more sustainable. The best way is to combine great ideas and innovative measures with political support. This Special Issue consists of six articles analyzing the impact of SUMPs. Innovative measures have been proposed to change urban transport systems towards sustainability: Chinese research has analyzed the tourist flow of Tibet using innovative technologies: mobile phone data, visualizations using GIS, and social networks. Lithuanian authors proposed three autonomous car travel development concepts that should become a conceptual tool in the development of ITS and C-ITS. An English scientific paper is based on a review of local transport policy documents from 13 cities in four countries. Most cities seek to reduce car travel as a proportion of trips. Experience from Slovenia shows that the comprehensive traffic calming approach has positive effects and contributes to achieving sustainable mobility. Korean researchers used the GINI coefficient to evaluate the bus system to identify bus nodes in order of importance. The last article described that multicriteria decision-making methods have been successfully used for assessing the effectiveness of sustainable transport systems, and a universal evaluation model was proposed
A cloud resource management model for the creation and orchestration of social communities
Managing resources, context and data in mobile clouds is a challenging task. Specific aspects of spontaneity, large interaction space and dynamic interaction share a metaphorical resemblance to chemistry, chemical reactions and solutions. In this paper, it is argued that by adopting a nature-inspired chemical computing model, a mobile cloud resource management model can be evolved to serve as the basis for novel service modelling and social computing in mobile clouds. To support the argument, a chemistry inspired computation model, Chemistry for Context Awareness (C2A), is extended with Higher Order Chemical Language (HOCL) and High Level Petri-net Graph (HLPNG) formalisms. A scenario and simulation-based evaluation of the proposed model, focusing on two applications dynamic service composition and social communities identification, is also presented in this paper. The formal encoding of C2A validates its assumptions, enabling formal execution and analysis of context-based interactions that are derived using C2A principles
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Identification and prediction of abnormal behaviour activities of daily living in intelligent environments
The aim of this research is to investigate efficient mining of useful information from a sensor network forming an Ambient Intelligence (AmI) environment. In this thesis, we investigate methods for supporting independent living of the elderly (and specifically patients who are suffering from dementia) by means of equipping their home with a simple sensor network to monitor their behaviour and identify their Activities of Daily Living (ADL). Dementia is considered to be one of the most important causes of disability in the elderly. Mostpatients would prefer to use non-intrusive technology to help them tomaintain their independence. Such monitoring and prediction would allow the caregiver to see any trend in the behaviour of the elderly person and to be informed of any abnormal behaviour
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A study of energy-related occupancy activities in a sample of monitored domestic buildings in the UK
Domestic energy use is determined by multiple non-technological factors, such as the occupants’ lifestyle and activities, which can even offset the effect from energy-efficiency technologies. Acquiring the actual occupancy data relating to energy use in a uniform format to generate comparable and representative information is challenging. Projects that seek to address this issue, such as the Retrofit for the Future and Building Performance Evaluation programmes of the Technology Strategy Board in the UK, usually require major investment. Long-term monitoring and longitudinal observation are two major features in these major investment projects. The former approach refers to the frequent measurement of indoor / outdoor environments and energy use conducted over at least two heating seasons, in line with the whole-house carbon and energy monitoring protocol of the Energy Saving Trust (2011). The latter approach, longitudinal observation, refers to observations conducted on the same group of individuals over an extended study period of years or decades to examine changes over time (Bryman, 2012). The majority of existing households and associated stakeholders that could potentially benefit from the investigation of energy-related occupancy activities cannot feasibly be involved in projects requiring major investment
ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.
The domain of materials for design is changing under the influence of an increased technological
advancement, miniaturization and democratization. Materials are becoming connected,
augmented, computational, interactive, active, responsive, and dynamic. These are ICS
Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the
world are experimenting with these new materials, there is the need to reflect on their
potentials and impact on design. This paper is a first step in this direction: to interpret and
describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge
Educational Technology and Related Education Conferences for June to December 2015
The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next
Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living
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
Teaching and Professional Fellowship Report 2007-2008 : Make the Numbers Count, Improving students' learning experiences through an analysis of Library and Learning Resources data
Feedback on Library and Learning Resources (LLR) services and support at the University of the Arts London (UAL) is received from a very extensive range of sources. However, although a considerable quantity of information is
available for analysis, it remains very difficult for LLR staff to extract meaningful data from these numerous sources, which can reveal, in depth, the true, individual student experience of LLR services. LLR is aware that there is a lot that is not known, or is not being asked of our students, about their individual experience, which could prove extremely helpful for effective decision-making and service design and delivery. In particular, LLR is interested in identifying and resolving issues of access and support for students currently studying at UAL who are from under represented groups in higher education.
Make the numbers counts has enabled the Fellowship Team to move beyond looking at usage figures, gate counts, numbers of issue and renewal transactions, borrower numbers and percentage satisfaction levels, and to extend and add to our knowledge-base of what students at UAL really think and feel about academic libraries. It has enabled us to explore how individual students make use of the services and support provided by LLR. The qualitative data collected through this project has been compared to existing sources of information and to staff experience and understanding of the issues raised, to see if the findings of our research challenge or correlate with other evidence which relates to LLR use and non-use. The focus of the Fellowship has been to explore the particular experience of students who are the first in their family to go to university and who are currently studying at Camberwell, Chelsea and Wimbledon (CCW) and the London College of Communication. Research has shown that this group of students is important amongst the range of students who are now entering higher education through the expansion of participation in university and
tertiary-level study. Talking to first generation students was a key element of the project and the greatest source of learning for the Project Team
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