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Introduction: The COVID-19 pandemic brought a strong discussion about telerehabilitation, especially in countries where its practice was not allowed previous the pandemic. In the neuropediatric rehabilitation field evidence of the effectiveness of this modality of assistance and data about provider and family’s perceptions are scarce. Objective: To evaluate the characteristics of telerehabilitation physical therapy consultations for children with neurological diseases during the COVID-19 pandemic and report the perceptions of physical therapists about this modality of assistance. Methods: An online survey was created and administered to Brazilian neuropediatric physical therapists. The survey consisted of demographics data, questions about the characteristics of telerehabilitation consultations, and physical therapists’ perception of telerehabilitation assistance. Results: From July to September 2020, 394 physical therapists responded to the questionnaire. The most of them took 21 to 30 minutes per telemonitoring session (26.9%), offered one session per week (61.0%), and used electronic messages as the main method of delivery (39.9%). The main difficulty reported was caregivers’ lack of understanding about handling during sessions (41.2%) and problems with internet connection (16.7%). Additionally, most of them reported that it is highly important important to provide healthcare via telerehabilitation to the economically disadvantaged. Conclusion: The findings of this study provide insight into the complex and challenging process of telerehabilitation assistance during the first wave of the COVID-19 pandemic. Despite the reported challenges, telerehabilitation can be an opportunity to better understand children’s activities and participation in home context. Resumo Introdução: A pandemia da COVID-19 trouxe uma forte discussão sobre o uso da telerreabilitação, especialmente em países onde a sua prática ainda não havia sido permitida antes da pandemia. Na área da reabilitação neuropediátrica as evidências sobre a efetividade dessa modalidade de atendimento e informações sobre as percepções dos provedores e família são escassas. Objetivo: Avaliar as características das consultas fisioterapêuticas por telerreabilitação em crianças com condições neurológicas durante a pandemia de COVID-19 e relatar as percepções dos fisioterapeutas sobre essa modalidade de atendimento. Métodos: Um questionário online foi criado e administrado para fisioterapeutas neuropediátricos brasileiros. O questionário consistiu em dados demográficos, questões sobre as características das consultas por telerreabilitação e percepção dos fisioterapeutas sobre a assistência via telerreabilitação. Resultados: Entre julho e setembro de 2020, 394 fisioterapeutas responderam o questionário. A maioria dos fisioterapeutas levaram de 21 a 30 minutos por sessão (26,9%), ofereceram uma sessão por semana (61,0%) e usaram mensagens eletrônicas como principal método de entrega (39,9%). A principal dificuldade reportada foi a falta de entendimento sobre os manuseios durante as sessões (41,2%) e problemas com a internet (16,7%). Além disso, a maioria reportou ser muito importante prover cuidados de saúde por telerreabilitação para a população economicamente menos favorecida. Conclusão: Os dados apresentados fornecem um entendimento do processo complexo e desafiador das consultas por telerreabilitação durante a primeira onda da pandemia da COVID-19. Apesar dos desafios reportados, a telerreabilitação pode ser uma oportunidade para melhor entender as atividades e participação das crianças no contexto do lar.

Introduction: The COVID-19 pandemic brought a strong discussion about telerehabilitation, especially in countries where its practice was not allowed previous the pandemic. In the neuropediatric rehabilitation field evidence of the effectiveness of this modality of assistance and data about provider and family’s perceptions are scarce. Objective: To evaluate the characteristics of telerehabilitation physical therapy consultations for children with neurological diseases during the COVID-19 pandemic and report the perceptions of physical therapists about this modality of assistance. Methods: An online survey was created and administered to Brazilian neuropediatric physical therapists. The survey consisted of demographics data, questions about the characteristics of telerehabilitation consultations, and physical therapists’ perception of telerehabilitation assistance. Results: From July to September 2020, 394 physical therapists responded to the questionnaire. The most of them took 21 to 30 minutes per telemonitoring session (26.9%), offered one session per week (61.0%), and used electronic messages as the main method of delivery (39.9%). The main difficulty reported was caregivers’ lack of understanding about handling during sessions (41.2%) and problems with internet connection (16.7%). Additionally, most of them reported that it is highly important important to provide healthcare via telerehabilitation to the economically disadvantaged. Conclusion: The findings of this study provide insight into the complex and challenging process of telerehabilitation assistance during the first wave of the COVID-19 pandemic. Despite the reported challenges, telerehabilitation can be an opportunity to better understand children’s activities and participation in home context.

Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Real time series sometimes exhibit various types of "irregularities": missing observations, observations collected not regularly over time for practical reasons, observation times driven by the series itself, or outlying observations. However, the vast majority of methods of time series analysis are designed for regular time series only. A particular case of irregularly spaced time series is that in which the sampling procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modelled and the times of the observations. In this work, we propose a model in which the sampling design depends on all past history of the observed processes. Taking into account the natural temporal order underlying available data represented by a time series, then a modelling approach based on evolutionary processes seems a natural choice. We consider maximum likelihood estimation of the model parameters. Numerical studies with simulated and real data sets are performed to illustrate the benefits of this model-based approach., The authors acknowledge Foundation FCT (FundacAo para a Ciencia e Tecnologia) as members of the research project PTDC/MAT-STA/28243/2017 and Center for Research & Development in Mathematics and Applications of Aveiro University within project UID/MAT/04106/2019.

There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature and solid field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic. published

Na maior parte da história mundial e brasileira, as mulheres estiveram afastadas ou com ínfima representação em vários segmentos da sociedade. O presente artigo, fruto de um dedicado trabalho apresentado em uma mostra científica de uma escola pública brasileira, buscou analisar a participação feminina nas esferas científica, política e esportiva, bem como discorrer sobre a importância do protagonismo juvenil no ensino médio. Nossos resultados mostram que apesar da análise geral nas três áreas estudadas confirmar participação ou representação feminina inferior à masculina, essas diferenças são mais acentuadas no espectro científico e político do que na esfera esportiva. Destacamos também a importância da valorização e incentivo ao protagonismo juvenil feminino no ensino médio com vistas a contribuir para a redução das desigualdades sociais entre homens e mulheres nessas áreas e construir uma sociedade mais justa e igualitária.

This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series. published

This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM. HRV recordings from the Noltisalis database are used for training and testing this approach. The results indicate that the procedure provides accuracy scores in the range of 86.7% to 90.0% on the test set. published

Background: Conventional acrylic resin is prone to microbial colonization and may cause inflammatory and allergic response. Aims: This study aims to research the initial microbial adhesion in situ and tissue response to an acrylic resin used in prosthodontics. Materials and Methods: Disks of a commercial acrylic resin were prepared and included on the surface of individual intraoral splints fabricated for 50 participants. The splints were used for 4 h, under clinical conditions. Beforehand, each participant was swabbed to provide a control for microbiological comparison. A cytological control sample was also taken from the palate. After the time elapsed, each splint was removed and growth of anaerobes, aerobes, Pseudomonas, oral streptococci, staphylococci, yeasts, and Streptococcus mutans was determined by plate counts and compared to the oral microbiome. A cytological sample was taken from the contact zone, stained using the Papanicolaou technique, analyzed in light microscopy, and classified accordingly. Means and standard deviations were calculated, and a nonparametric Wilcoxon test was employed to compare experimental groups. The significance level was set at 0.05 (95% confidence interval, and statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) version 20.0. Results: Nuclear-cytoplasm ratio increase was found in 84% of the smears retrieved from the contact zone. Over 60% showed nuclear alterations. With exception to yeasts and Pseudomonas, all microbial groups colonized the resin. No statistically significant differences were found between the oral microbiome and the acrylic resin's colonization except regarding yeasts (P > 0.05). Conclusions: Cellular alterations were found but a diagnosis of inflammation is inconclusive. Microbial adhesion to the acrylic resin was substantial, with multiple species adhering.

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale entropy (MSE), has been proven to be unsuitable in the presence of short multivariate time series to be analyzed at long time scales. This work aims at overcoming these issues via the introduction of a new method for the assessment of the multiscale complexity of multivariate time series. The method first exploits vector autoregressive fractionally integrated (VARFI) models to yield a linear parametric representation of vector stochastic processes characterized by short- and long-range correlations. Then, it provides an analytical formulation, within the theory of state-space models, of how the VARFI parameters change when the processes are observed across multiple time scales, which is finally exploited to derive MSE measures relevant to the overall multivariate process or to one constituent scalar process. The proposed approach is applied on cardiovascular and respiratory time series to assess the complexity of the heart period, systolic arterial pressure and respiration variability measured in a group of healthy subjects during conditions of postural and mental stress. Our results document that the proposed methodology can detect physiologically meaningful multiscale patterns of complexity documented previously, but can also capture significant variations in complexity which cannot be observed using standard methods that do not take into account long-range correlations.

Submitted by Isabel Pereira (isabel.pereira@ua.pt) on 2019-08-01T16:12:20Z No. of bitstreams: 1 jtsa_12439.pdf: 377141 bytes, checksum: cf0faf6356c9e39f522c02a11515d08a (MD5) Approved for entry into archive by Rita Gonçalves (ritaisabel@ua.pt) on 2019-08-05T14:51:58Z (GMT) No. of bitstreams: 1 jtsa_12439.pdf: 377141 bytes, checksum: cf0faf6356c9e39f522c02a11515d08a (MD5) Made available in DSpace on 2019-08-05T14:51:58Z (GMT). No. of bitstreams: 1 jtsa_12439.pdf: 377141 bytes, checksum: cf0faf6356c9e39f522c02a11515d08a (MD5) Previous issue date: 2019-09 This work is partially supported by Portuguese funds through CIDMA and the Portuguese Foundation for Science and Technology (FCT-Fundação para a Ciência e a Tecnologia), within project UID/MAT/04106/2019. published

In Cusco, Peru, and South America in general, there is a dearth of travelers' diarrhea (TD) data concerning the clinical features associated with enteropathogen-specific infections and destination-specific risk behaviors. Understanding these factors would allow travel medicine providers to tailor interventions to patients' risk profiles and travel destination. To characterize TD etiology, evaluate region-specific TD risk factors, and examine relationships between preventive recommendations and risk-taking behaviors among medium- to long-term travelers' from high-income countries, we conducted this case-case analysis using 7 years of prospective surveillance data from adult travelers' presenting with TD to a physician in Cusco. At the time of enrollment, participants provided a stool sample and answered survey questions about demographics, risk behaviors, and the clinical features of illness. Stool samples were tested for norovirus (NV), bacteria, and parasites using conventional methods. Data obtained were then analyzed using case-case methods. NV (14%), enterotoxigenic Escherichia coli (11%), and Campylobacter (9%), notably ciprofloxacin-resistant Campylobacter, were the most frequently identified pathogens among adults with TD. Coinfection with multiple enteropathogens occurred in 5% of cases. NV caused severe disease relative to other TD-associated pathogens identified, confining over 90% of infected individuals to bed. Destination-specific risk factors include consumption of the local beverage "chicha," which was associated with Cryptosporidium infection. Preventive interventions, such as vaccines, directed against these pathogens could significantly reduce the burden of TD.

A new first-order integer-valued moving average, INMA(1), model based on the negative binomial thinning operation defined by Risti et al. [21] is proposed and characterized. It is shown that this model has negative binomial (NB) marginal distribution when the innovations follow an NB distribution and therefore it can be used in situations where the data present overdispersion. Additionally, this model is extended to the bivariate context. The Generalized Method of Moments (GMM) is used to estimate the unknown parameters of the proposed models and the results of a simulation study that intends to investigate the performance of the method show that, in general, the estimates are consistent and symmetric. Finally, the proposed model is fitted to a real dataset and the quality of the adjustment is evaluated. (c) 2019, Springer Nature Switzerland AG.

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then its complexity is evaluated in terms of conditional entropy. Within this framework, our approach makes use of linear fractionally integrated autoregressive (ARFI) models to derive analytical expressions for the information storage computed at multiple timescales. Specifically, we exploit state space models to provide the representation of lowpass filtered and downsampled ARFI processes, from which information storage is computed at any given timescale relating the process variance to the prediction error variance. This enhances the practical usability of multiscale information storage, as it enables a computationally reliable quantification of a complexity measure which incorporates the effects of LRC together with that of short-term dynamics. The proposed measure is first assessed in simulated ARFI processes reproducing different types of autoregressive dynamics and different degrees of LRC, studying both the theoretical values and the finite sample performance. We find that LRC alter substantially the complexity of ARFI processes even at short timescales, and that reliable estimation of complexity can be achieved at longer timescales only when LRC are properly modeled. Then, we assess multiscale information storage in physiological time series measured in humans during resting state and postural stress, revealing unprecedented responses to stress of the complexity of heart period and systolic arterial pressure variability, which are related to the different role played by LRC in the two conditions. published

Mean sea level is a variable of considerable interest in meteorological and oceanographic studies, particularly long-term sea level variation and its relation to climate changes. This study concerns the analysis of monthly mean sea level data from tide gauge stations in the Northeast Atlantic with long and continuous records. Much research effort on mean sea level studies has been focused on identifying long-term linear trends, usually estimated through least-squares fitting of a deterministic function. Here, we estimate nonparametric and robust trends using lowess, a robust smoothing procedure based on locally weighted regression. This approach is more flexible than a linear trend to describe the deterministic part of the variation in tide gauge records, which has a complex structure. A common trend pattern of reduced sea levels around 1975 is found in all the analysed records and interpreted as the result of hydrological and atmospheric forcing associated with drought conditions at the tide gauge sites. This feature is overlooked by a linear regression model. Moreover, nonlinear deterministic behaviour in the time series, such as the one identified, introduces a bias in linear trends determined from short and noisy records.Key words. Oceanography: physical (sea level variations); Hydrology (water balance)

The presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time points of outlier occurrence are proposed. The effectiveness of the proposed methods is illustrated with synthetic as well as with an observed dataset.

Background:There are limited data on the burden of disease posed by influenza in low- and middle-income countries. Furthermore, most estimates of influenza disease burden worldwide rely on passive sentinel surveillance at health clinics and hospitals that lack accurate population denominators. Methods:We documented influenza incidence, seasonality, health-system utilization with influenza illness, and vaccination coverage through active community-based surveillance in 4 ecologically distinct regions of Peru over 6 years. Approximately 7200 people in 1500 randomly selected households were visited 3 times per week. Naso- and oropharyngeal swabs were collected from persons with influenza-like illness and tested for influenza virus by real-time reverse-transcription polymerase chain reaction. Results:We followed participants for 35353 person-years (PY). The overall incidence of influenza was 100 per 1000 PY (95% confidence interval [CI], 97-104) and was highest in children aged 2-4 years (256/1000 PY [95% CI, 236-277]). Seasonal incidence trends were similar across sites, with 61% of annual influenza cases occurring during the austral winter (May-September). Of all participants, 44 per 1000 PY (95% CI, 42-46) sought medical care, 0.7 per 1000 PY (95% CI, 0.4-1.0) were hospitalized, and 1 person died (2.8/100000 PY). Influenza vaccine coverage was 27% among children aged 6-23 months and 26% among persons aged ≥65 years. Conclusions:Our results indicate that 1 in 10 persons develops influenza each year in Peru, with the highest incidence in young children. Active community-based surveillance allows for a better understanding of the true burden and seasonality of disease that is essential to plan the optimal target groups, timing, and cost of national influenza vaccination programs.

In the last decade, several HRV based novel methodologies for describing and assessing heart rate dynamics have been proposed in the literature with the aim of risk assessment. Such methodologies attempt to describe the non-linear and complex characteristics of HRV, and hereby the focus is in two of these characteristics, namely long memory and heteroscedasticity with variance clustering. The ARFIMA-GARCH modeling considered here allows the quantification of long range correlations and time-varying volatility. ARFIMA-GARCH HRV analysis is integrated with multimodal brain monitoring in several acute cerebral phenomena such as intracranial hypertension, decompressive craniectomy and brain death. The results indicate that ARFIMA-GARCH modeling appears to reflect changes in Heart Rate Variability (HRV) dynamics related both with the Acute Brain Injury (ABI) and the medical treatments effects. (c) 2017, Springer International Publishing AG.

We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645–650, 1985. doi: 10.1111/j.1752-1688.1985.tb05379.x ). Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.

This paper introduces new classes of bivariate time series models being useful to fit count data time series with a finite range of counts. Motivation comes mainly from the comparison of schemes for monitoring tourism demand, stock data, production and environmental processes. All models are based on the bivariate binomial distribution of Type II. First, a new family of bivariate integer-valued GARCH models is proposed. Then, a new bivariate thinning operation is introduced and explained in detail. The new thinning operation has a number of advantages including the fact that marginally it behaves as the usual binomial thinning operation and also that allows for both positive and negative cross-correlations. Based upon this new thinning operation, a bivariate extension of the binomial autoregressive model of order one is introduced. Basic probabilistic and statistical properties of the model are discussed. Parameter estimation and forecasting are also covered. The performance of these models is illustrated through an empirical application to a set of rainy days time series collected from 2000 up to 2010 in the German cities of Bremen and Cuxhaven. published

In this paper the integer-valued autoregressive model of order one, contaminated with additive outliers is studied in some detail. Moreover, parameter estimation is also addressed. Supposing that the timepoints of the outliers are known but their sizes are unknown, we prove that the conditional least squares (CLS) estimators of the offspring and innovation means are strongly consistent. In contrast, however, the CLS estimators of the outliers’ sizes are not strongly consistent, although they converge to a random limit with probability 1. We also prove that the joint CLS estimator of the offspring and innovation means is asymptotically normal. Conditionally on the values of the process at the timepoints neighboring to the outliers’ occurrences, the joint CLS estimator of the sizes of the outliers is also asymptotically normal.

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2012

Trabalho realizado sob a orientação deTelmo Silva Tese de Mestrado Integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 2012

Estágio realizado no SAPO Lab, e orientado pelo Prof. Luís Sarmento Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 2011

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 2011

With the growth of Internet and especially, the proliferation of social media services, an opportunity has emerged for greater social and technological integration of the elderly. However, the adoption of new technologies by this segment of the population is not always straightforward mainly due to the physical and cognitive difficulties that are typically associated with ageing. Thus, for elderly to take advantage of new technologies and services that can help improve their quality of life, barriers must be broken by designing solutions with those needs in mind from the start. The aim of this work is to verify whether Multimodal Human-Computer Interaction (MHCI) systems designed with Universal Accessibility principles, taking into account elderly specific requirements, facilitate the adoption and access to popular Social Media Services (SMSs) and Audiovisual Communication Services, thus potentially contributing to the elderly social and technological integration. A user study was initially conducted in order to learn about the limitations and requirements of elderly people with existing HCI, concerning particularly SMSs and Audiovisual Communication Services, such as Facebook or Windows Live Messenger (WLM). The results of the study, basically a set of new MHCI requirements, were used to inform further development and enhancement of a multimodal prototype previously proposed for mobility-impaired individuals, now targeting the elderly. The prototype allows connecting users with their social networks through a text, audio and video communication service and integrates with SMSs, using natural interaction modalities, like speech, touch and gesture. After the development stage a usability evaluation study was conducted. The study reveals that such multimodal solution could simplify accessibility to the supported services, through the provision of simpler to use interfaces, by adopting natural interaction modalities and by being more satisfying to use by the elderly population, than most of the current graphical user interfaces for those same services, such as Facebook. Com o crescimento da Internet e, especialmente, das redes sociais surge a oportunidade para uma maior integração social e tecnológica dos idosos. No entanto, a adoção de novas tecnologias por essa população nem sempre é simples, principalmente devido às dificuldades físicas e cognitivas que estão associadas com o envelhecimento. Assim, e para que os idosos possam tirar proveito das novas tecnologias e serviços que podem ajudar a melhorar sua qualidade de vida, essas barreiras devem ser ultrapassadas desenhando soluções de raiz com essas necessidades em mente. O objetivo deste trabalho é verificar se interfaces humano-computador multimodais desenhadas com base em princípios de Acessibilidade Universal, tendo em conta requisitos específicos da população idosa, proporcionam um acesso simplificado a serviços de média social e serviços de comunicação audiovisuais, potencialmente contribuindo para a integração social e tecnológica desta população. Um estudo com utilizadores foi inicialmente conduzido a fim de apurar as necessidades especiais desses utilizadores com soluções de software, mais especificamente serviços de média social e serviços de conferência, como o Facebook ou o Windows Live Messenger. Os resultados do estudo foram utilizados para planear o desenvolvimento de um protótipo multimodal proposto anteriormente para utilizadores com mobilidade reduzida. Este permite ligar utilizadores às suas redes sociais através de um serviço de conferência por texto, áudio e vídeo, e um serviço integrado de média social, usando modalidades de interação natural, como o toque, fala e gestos. Após a fase de desenvolvimento foi realizado um estudo de usabilidade. Esse estudo revelou que este tipo de soluções pode simplificar a acessibilidade aos serviços considerados, dado ter interfaces mais simples, por adotar modalidades de interação mais naturais e por ser mais gratificante do que a maioria das interfaces gráficas atuais para os mesmos serviços, como por exemplo o Facebook.

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto, Microsoft Language Development Center. 2010

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto, Microsoft Language Development Center. 2010