Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Factos & Números
000
Apresentação

Centro de Investigação em Engenharia Biomédica

No C-BER os nossos objetivos vão desde a criação de conhecimento interdisciplinar que permita a inovação e a transferência de tecnologia com impacto económico até ao desenvolvimento de produtos, ferramentas e métodos para a prevenção e deteção precoce de diferentes tipos de doenças, problemas relacionados com o envelhecimento, reabilitação humana, fisioterapia ou avaliação funcional.

Pretendemos ainda contribuir para o desenvolvimento de neuro-tecnologias avançadas na fronteira entre a engenharia e a neurologia, assim como promover parcerias estratégicas com parceiros clínicos, institutos de investigação e fomentar a cooperação internacional.

Desenvolvemos investigação em três áreas distintas: Imagem Biomédica, Bioinstrumentação e Neuroengenharia.

Últimas Notícias

Projeto recorre à fotónica e à IA para prever evolução da infeção por COVID-19

Um grupo de investigadores do Centro de Fotónica Aplicada (CAP), do Centro de Engenharia Biomédica (C-BER) e do Centro para a Inovação, Tecnologia e Empreendedorismo (CITE) do INESC TEC, e da spin-off iLoF está a estudar a implementação de uma ferramenta baseada em medicina personalizada, rápida e de baixo custo, para prever a evolução da infeção viral por Covid-19 nos pacientes.

11 maio 2020

INESC TEC com cinco projetos para combate à COVID-19 aprovados pela FCT

O INESC TEC viu cinco projetos aprovados pela linha 'RESEARCH 4 COVID-19' da Fundação para a Ciência e Tecnologia (FCT), três enquanto entidade proponente, num total de 85 mil euros. Os projetos vão desenvolver soluções destinadas ao combate da pandemia por COVID-19.

27 abril 2020

Redes de Sistemas Inteligentes

INESC TEC desenvolve tecnologia ótica inteligente que distingue e deteta células cancerígenas

O potencial do dispositivo iLoF (Lab on Fiber) para identificar células cancerígenas com diferentes perfis é destacado num artigo publicado numa das revistas científicas da "Nature".

12 março 2020

Redes de Sistemas Inteligentes

«Best Practical Paper Award» para investigação INESC TEC no rastreio de retinopatia

“EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection” é o título do artigo científico que mereceu o Best Practical paper award na MVA 2019 (International Conference on Machine Vision Applications) realizada em Tóquio, Japão.

14 fevereiro 2020

Redes de Sistemas Inteligentes

INESC TEC tem primeira patente concedida na China

O INESC TEC tem, pela primeira vez, uma patente concedida na China. O feito foi alcançado com a tecnologia C4Mir (“Control Module for Multiple Mixed-Signal Resources Management”), que já tinha visto os seus pedidos de patente concedidos na Europa, EUA, Coreia do Sul e Japão.

30 dezembro 2019

Tópicos de interesse
023

Projetos Selecionados

CAIRUS

COVID-19 Artificial Intelligence-based Risk Unified Stratification tool for clinical management

2020-2020

CXR_AI4COVID19

Chest Radiography-based AI for Supporting ClinicalDecision on Covid-19

2020-2020

TAMI

Transparent Artificial Medical Intelligence

2020-2023

BioNanoTech

Serviços de apoio técnico - iLoF spin-off

2020-2021

MRI_Simulator

Aluguer do Simulador de Ressonância Magnética à Faculdade de Psicologia e de Ciências da Educação da Universidade do Porto

2019-2020

WalkingPAD

Patient education on a quantified supervised home-based exercise therapy to improve walking ability in patients with peripheral arterial disease and intermittent claudication

2019-2021

Serv_Neuro

Consultoria Insignals Neurotech no âmbito do programa EIT Health Startups Meets Pharma 2019

2019-2019

LUCAS

Rastreio do cancro do pulmão - uma metodologia não invasiva para o diagnóstico precoce

2018-2021

PERFECT

Equivalência percetual em realidade virtual para treino autêntico

2018-2020

TexBoost

Less Commodities more Specialities

2017-2020

LNDetector

Sistema Automático de Deteção, Segmentação e Classificação de Nódulos Pulmonares em Imagens Tomografia Computadorizada

2016-2019

SCREEN-DR

Plataforma de Análise de Imagem e de Aprendizagem Computacional para a Inovação no Rastreio da Retinopatia Diabética

2016-2020

Bio-Early

Projeto Vital Sticker no âmbito do Contrato Programa

2015-2018

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

SMILES

TEC4Growth - RL SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

VR2Market

VR2Market: Desenvolvimento dum Produto para Monitorização Móvel e Vestível da Saúde de Profissionais de Primeira Resposta e de outras Profissões de Risco

2014-2019

STePMotion

Componentes espácio-temporais do processamento de informação motora e sensorial

2014-2015

EcoDrive

Condução Ecológica e Gestão Inteligente de Frotas

2014-2015

Re-Learning

Re-aprendizagem motora através do uso da fusão de informação de sensores

2014-2015

VitalResponder2

Gestão inteligente de eventos críticos de stress, fadiga e intoxicação pelo fumo no combate a fogos florestais

2013-2015

ASD-MD

Doenças do Movimento na Perturbação do Espetro Autista

2013-2015

HERMES

Sistema de interactividade entre consumidores de conteúdos digitais

2013-2015

Equipa
003

Laboratórios

Laboratório de Imagem Biomédica

Laboratório Avançado de Neuroengenharia e Deteção Humana

Laboratório de BioInstrumentação

Publicações

C-BER Publicações

Ler todas as publicações

2020

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

Autores
Martins, M; Gomes, P; Oliveira, C; Coimbra, M; da Silva, HP;

Publicação
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Abstract
Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure.

2020

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Autores
Pires, IM; Marques, G; Garcia, NM; Florez Revuelta, F; Canavarro Teixeira, M; Zdravevski, E; Spinsante, S; Coimbra, M;

Publicação
ELECTRONICS

Abstract
The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs' identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

2020

Gaussian Mixture Model Based Probabilistic Modeling of Images for Medical Image Segmentation

Autores
Riaz, F; Rehman, S; Ajmal, M; Hafiz, R; Hassan, A; Aljohani, NR; Nawaz, R; Young, R; Coimbra, M;

Publicação
IEEE Access

Abstract
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. © 2013 IEEE.

2020

Virtual reality in training: an experimental study with firefighters

Autores
Narciso, D; Melo, M; Raposo, JV; Cunha, J; Bessa, M;

Publicação
Multimedia Tools and Applications

Abstract
Training with Virtual Reality (VR) can bring several benefits, such as the reduction of costs and risks. We present an experimental study that aims to evaluate the effectiveness of a Virtual Environment (VE) to train firefighters using an innovative approach based on a Real Environment (RE) exercise. To measure the VE’s effectiveness we used a Presence Questionnaire (PQ) and participant’s cybersickness, stress and fatigue. Results from the PQ showed that participants rated the VE with high spatial presence and moderate realness and immersion. Signs of stress, analyzed from participant’s Heart-Rate Variability, were shown in the RE but not in the VE. In the remaining variables, there was only an indicative difference for fatigue in the RE. Therefore, the results suggest that although our training VE was successful in giving participants spatial presence and in not causing cybersickness, its realness and immersion provided were not enough to provoke a similar RE response. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

2020

Subject Identification Based on Gait Using a RGB-D Camera

Autores
Rocha, AP; Fernandes, JM; Choupina, HMP; Vilas Boas, MC; Cunha, JPS;

Publicação
Advances in Intelligent Systems and Computing

Abstract
Biometric authentication (i.e., verification of a given subject’s identity using biological characteristics) relying on gait characteristics obtained in a non-intrusive way can be very useful in the area of security, for smart surveillance and access control. In this contribution, we investigated the possibility of carrying out subject identification based on a predictive model built using machine learning techniques, and features extracted from 3-D body joint data provided by a single low-cost RGB-D camera (Microsoft Kinect v2). We obtained a dataset including 400 gait cycles from 20 healthy subjects, and 25 anthropometric measures and gait parameters per gait cycle. Different machine learning algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines, multilayer perceptron, and multilayer perceptron ensemble. The algorithm that led to the model with best trade-off between the considered evaluation metrics was the random forest: overall accuracy of 99%, class accuracy of 100±Â0%, and F 1 score of 99±Â2%. These results show the potential of using a RGB-D camera for subject identification based on quantitative gait analysis. © 2020, Springer Nature Switzerland AG.

Factos & Números

4Capítulos de livros

2019

29Artigos em conferências indexadas

2019

22Artigos em revistas indexadas

2019

Contactos
C-BER
Centro de Investigação em Engenharia Biomédica
João Paulo Cunha

Responsável

+351222094106

joao.p.cunha@inesctec.pt