46 research outputs found

    Personalization of convolutional neural networks within the stress detection task using heart rate variability data

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    Stress detection is an active area of research with important implications for personal, occupational, and social health. Most modern approaches use features computed from multiple sensor modalities, i.e., grouping different types of data from multiple sources for processing. These include electrocardiogram, electrodermal activity, electromyogram, skin temperature, respiration, accelerometer data, etc. Also, traditional machine learning algorithms (decision tree, discriminant analysis, support vector machine, etc.) or fully-connected neural networks are mostly used. Using these methods requires large amounts of data. Researchers are considering different approaches to personalization or generalization of models relative to subjects, namely subject-independent and subject-dependent (initially personal or adapted) models. The aim of the presented work is to develop a method for detecting stress based on heart rate variability data, taking into account the process of personalization of neural networks. The use of a convolutional neural network is proposed. The dependence of accuracy on the length of the input signal is studied. The dependence of accuracy on the data dimensionality reduction layer (one-dimensional convolutional layer, maximizing and averaging pooling) used in the network is also considered. The importance of personalizing models is demonstrated to significantly increase the accuracy of models of specific subjects. It is shown that the proposed method, based on 60 intervals between heartbeats, makes it possible to binary determine whether a person is under stress. Personalization allowed increasing the accuracy from 91.8 % to 98.9 ± 2.6 %. The F1-score value increased from 0.907 to 0.983 ± 0.038. The proposed personalized networks can be used in systems for monitoring the functional state of a person. They can also be used as part of a system that grants or restricts access to private resources based on whether a person is currently at rest

    Beyond mobile apps: a survey of technologies for mental well-being

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    Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time

    A state-of-the-art of physics-informed neural networks in engineering

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    Técnicas de machine learning vêm ganhando cada vez mais espaço no cenário industrial no intuito de converter o crescente fluxo de informação (data) em melhorias de processos. Entre tais técnicas, as redes neuronais se destacam devido à sua capacidade de aproximador universal de funções, cuja performance pode ser enriquecida ao se fornecer conhecimentos físicos prévios: tem-se, então, o desenvolvimento das Physics-informed neural networks (PINN). Nesse contexto e observando-se um “gap” na produção de trabalhos relacionados ao tema e da difusão dessa temática na grade de formação dos cursos da Escola de Química, esse trabalho se propõe a realizar um estado da arte da técnica mencionada. Observou-se interesse particular das PINN para aplicações em mecânica dos fluidos e transferência de calor. Ademais, as PINN se mostram ferramentas importantes tanto para a resolução de problemas ditos “diretos” quanto “indiretos”. Por fim, através de exemplos práticos, constatou-se a capacidade de se aproximar funções de interesse particular na indústria química usando-se redes neurais sem nenhuma informação física do problema (obtenção do fator de atrito) e utilizando-se a equação diferencial que descreve o problema (resolução da equação de difusão em 1D)

    ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly

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    This article provides an overview of outstanding sessions that were (co)organised by the Allied Respiratory Professionals Assembly during the European Respiratory Society International Congress 2020, which this year assumed a virtual format. The content of the sessions was mainly targeted at allied respiratory professionals, including respiratory function technologists and scientists, physiotherapists, and nurses. Short take-home messages related to spirometry and exercise testing are provided, highlighting the importance of quality control. The need for quality improvement in sleep interventions is underlined as it may enhance patient outcomes and the working capacity of healthcare services. The promising role of digital health in chronic disease management is discussed, with emphasis on the value of end-user participation in the development of these technologies. Evidence on the effectiveness of airway clearance techniques in chronic respiratory conditions is provided along with the rationale for its use and challenges to be addressed in future research. The importance of assessing, preventing and reversing frailty in respiratory patients is discussed, with a clear focus on exercise-based interventions. Research on the impact of disease-specific fear and anxiety on patient outcomes draws attention to the need for early assessment and intervention. Finally, advances in nursing care related to treatment adherence, self-management and patients’ perspectives in asthma and chronic obstructive pulmonary disease are provided, highlighting the need for patient engagement and shared decision making. This highlights article provides readers with valuable insight into the latest scientific data and emerging areas affecting clinical practice of allied respiratory professionals.European CommissionFWOCenter for Innovative Care and Health Technology (ciTechCare) of the Polytechnic of Leiria - Fundacao para a Ciencia e Tecnologia (FCT) UIDB/05704/2020 UIDP/05704/202

    Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results

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    Humour is a substantial element of human affect and cognition. Its automatic understanding can facilitate a more naturalistic human-device interaction and the humanisation of artificial intelligence. Current methods of humour detection are solely based on staged data making them inadequate for 'real-world' applications. We address this deficiency by introducing the novel Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of about 11 hours of recordings. The Passau-SFCH dataset is annotated for the presence of humour and its dimensions (sentiment and direction) as proposed in Martin's Humor Style Questionnaire. We conduct a series of experiments, employing pretrained Transformers, convolutional neural networks, and expert-designed features. The performance of each modality (text, audio, video) for spontaneous humour recognition is analysed and their complementarity is investigated. Our findings suggest that for the automatic analysis of humour and its sentiment, facial expressions are most promising, while humour direction can be best modelled via text-based features. The results reveal considerable differences among various subjects, highlighting the individuality of humour usage and style. Further, we observe that a decision-level fusion yields the best recognition result. Finally, we make our code publicly available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset is available upon request.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible (Major Revision
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