Publications by authors named "David Belo"

Rheumatic and Musculoskeletal Diseases (RMDs) are very common and can negatively impact patients' quality of life. The current care of patients with RMDs is episodic, based on a few yearly doctor visits, which may not provide an adequate picture of the patient's condition. Researchers have hypothesized that RMDs could be passively monitored using smartphones or sensors, however, there are no datasets to support this development.

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children.

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Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications.

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The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN).

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Article Synopsis
  • The INSIDE system is a networked robot designed to engage actively in the therapy of children with autism spectrum disorders (ASD), aiming to improve interaction compared to traditional remote-operated robots.
  • It allows for complex, semi-unstructured interactions, making it suitable for therapeutic settings where flexibility is key, as opposed to rigidly controlled environments.
  • The paper discusses the supporting hardware and software as well as the design process, and shares findings from pilot and long-term studies at Hospital Garcia de Orta in Portugal, showcasing the system's effectiveness and autonomy.
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Background: Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field.

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The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree.

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Biosignals are usually contaminated with artifacts from limb movements, muscular contraction or electrical interference. Many algorithms of the literature, such as threshold methods and adaptive filters, focus on detecting these noisy patterns. This study introduces a novel method for noise and artifact detection in electrocardiogram based on time series clustering.

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