30 research outputs found
Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia:A State-of-the-Art Review
The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD
Brain-Computer Interface
Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems
SIS 2017. Statistics and Data Science: new challenges, new generations
The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data
Recent Applications in Graph Theory
Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks
Análise do testemunho ocular utilizando sinais de eletroencefalograma
The application of Brain Computer Interfaces techniques to vital crime witnesses
could and probably will be a key feature in the justice system.
Features from the electroencephalogram signals were extracted with information
detailing their domain (time or frequency), and their spacial scalp and
time placement. For both domains, two different classification pipelines were
applied in order to select the most relevant features: one to rank and select
the top features and another to recursively eliminate the least relevant feature.
The Support Vector Machine (linear and non-linear) is the classification model
included in the pipeline.
Further observations on the selected features by the applied techniques were
performed and discussed in relation to the available knowledge about face
recognition.
The present work provides an experimental study on the electroencephalogram
signals acquired from an experiment in which an array of subjects were
asked to identify both culprit and distractor being the culprit related to a previously
shown crime scene video.A aplicação de técnicas de Interfaces Cérebro-Computador a testemunhas
vitais de um crime pode e provavelmente será uma funcionalidade chave no
sistema de justiça.
Características de sinais provenientes de eletroencefalograma foram extraídas
com informações sobre o seu domínio (tempo ou frequência), e a sua
localização espacial e temporal. Para ambos os domínios, dois modelos de
classificação diferentes foram aplicados com vista a selecionar as características
mais relevantes: um para classificar, ordenar e selecionar as características
mais importantes e outro para eliminar recursivamente a característica
menos relevante. O modelo utilizado para classificação foi o Support Vector
Machine (linear e não linear).
Outras observações sobre as características selecionadas pelas técnicas aplicadas
foram realizadas e discutidas tendo em conta o conhecimento disponível
sobre reconhecimento facial.
O presente trabalho fornece um estudo experimental sobre os sinais de eletroencefalograma
adquiridos numa experiência na qual foi pedido a um grupo de
indivíduos para identificar tanto culpado como distrator, sendo que o culpado
estava relacionado a um vídeo de cenário de crime mostrado anteriormente.Mestrado em Engenharia de Computadores e Telemátic