Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Facts & Numbers
000
Presentation

Biomedical Engineering Research

At C-BER our main goals are the creation of interdisciplinary knowledge enabling innovation and technology transfer with economic impact; and also the development of products, tools and methods for the prevention and early detection of different types of diseases, aging-related impairments, or for human rehabilitation, physical therapy or functional assessment.

We also seek to contribute to the development of advanced neuro-technologies at the frontier of engineering and neurology, and to promote strategic partnerships with clinical partners, research institutes, and fostering international cooperation.

Our R&D activity is developed in three different areas: BioInstrumentation, Biomedical Imaging and NeuroEngineering.

Latest News
Networked Intelligent Systems

INESC TEC technology in the first neurostimulator in the world using long-term monitoring in epileptic patient

The new neurostimulator produced by the North-American company Medtronic relies on technology developed by INESC TEC’s Centre for Biomedical Engineering Research (C-BER), in order to measure the movements caused by epileptic events in 3D, and connect them with deep brain stimulation, as well as brain surface signals (EEG). This device was approved for human use in Europe in January this year, and it enables stimulating and capturing brain signals simultaneously, which can make all the difference in the patients’ quality of life. For the first time in the world, this neurostimulator was implanted in an epileptic patient, and used in a study on 3D EEG video technology in late July, at the University Hospital Centre of São João (CHUSJ).

14th September 2020

Networked Intelligent Systems

INESC TEC spin-off acknowledged as one of the most promising in the world in digital health

ILoF – Intelligent on Fiber, a spin-off from the University of Porto, established in INESC TEC and currently incubated at the Faculty of Medicine (FMUP), is among the 150 most promising start-ups in the field of digital health worldwide, according to a list provided by CB Insights, a North American company specialised in business analysis.

31st August 2020

Networked Intelligent Systems

INESC TEC developed artificial intelligence algorithms to support medical diagnosis

The TAMI project (Transparent Artificial Medical Intelligence), led by First Solutions and with the participation of INESC TEC through the Centre for Telecommunications and Multimedia (CTM) and the Centre for Biomedical Engineering Research (C-BER), aims to make medical diagnosis supported by Artificial Intelligence (AI) clearer and more reliable.

06th August 2020

Networked Intelligent Systems

INESC TEC researchers organised a conference on Image Analysis and Recognition

The ICIAR 2020 – 17th International Conference on Image Analysis and Recognition – took place between the June 24 and 26. The international event in the fields of Image Processing and Analysis, Computer Vision, Machine Learning and Medical Image Analysis has been held annually, alternating between Portugal and Canada. The 2020 edition was “100% digital”, with the collaboration of five researchers from INESC TEC.

09th July 2020

Project resorts to photonics and AI to predict the evolution of COVID-19 infection

A group of researchers from INESC TEC's Centre for Applied Photonics (CAP), Centre for Biomedical Engineering Research (C-BER) and Centre for Innovation Technology and Entrepreneurship (CITE), together with the spin-off iLoF, is studying the implementation of a quick and low-cost tool based on personalised medicine, in order to predict the evolution of  Covid-19 viral infection in patients.

11th May 2020

Interest Topics
026

Featured Projects

C4MiR_BIP_Proof

C4MiR_BIP_Proof_PROGRAMA DE ATRIBUIÇÃO DE APOIOS PARA PROVA DE CONCEITO DA UNIVERSIDADE DO PORTO

2020-2021

iiLab

Ampliação da Infraestrutura Tecnológica do INESC TEC para a Transformação Digital da Indústria

2020-2022

VitalPROVID

VitalPROVID

2020-2021

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

Lung cancer screening - A non-invasive methodology for early diagnosis

2018-2021

PERFECT

Perceptual equivalence in virtual reality for authentic training

2018-2020

TexBoost

Less Commodities more Specialities

2017-2020

LNDetector

Automatic Detection, Segmentation and Classification of Pulmonary Nodules System in Computed Tomography Images

2016-2019

SCREEN-DR

Image Analysis and Machine Learning Platform for Innovation in Diabetic Retinopathy Screening

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

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

2015-2019

VR2Market

VR2Market: Towards a Mobile Wearable Health Surveillance Product for First Response and other Hazardous Professions

2014-2019

STePMotion

Spatio-temporal components of the processing of sensorial and motor information

2014-2015

EcoDrive

Inteligent Eco Driving and Fleet Management

2014-2015

Re-Learning

Human motor re-learning by sensor information fusion

2014-2015

VitalResponder2

Intelligent management of critical events of stress, fatigue and smoke intoxication in forest firefighting

2013-2015

ASD-MD

Movement Disorders in Autistic Spectrum Disorders

2013-2015

HERMES

Interactive system for digital content consumers

2013-2015

Team
003

Laboratories

Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab

Publications

C-BER Publications

View all Publications

2020

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

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

Publication
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

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

Publication
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

Authors
Riaz, F; Rehman, S; Azad, MA; Hafiz, R; Hassan, A; Aljohani, NR; Nawaz, R; Young, RCD; Coimbra, MT;

Publication
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

Secure Triplet Loss for End-to-End Deep Biometrics

Authors
Pinto, JR; Cardoso, JS; Correia, MV;

Publication
2020 8th International Workshop on Biometrics and Forensics (IWBF)

Abstract

2020

Comparison of upper limb kinematics in two activities of daily living with different handling requirements

Authors
Mesquita, IA; Pereira da Fonseca, PFP; Borgonovo Santos, M; Ribeiro, E; Vieira Pinheiro, ARV; Correia, MV; Silva, C;

Publication
HUMAN MOVEMENT SCIENCE

Abstract
Introduction: Recently, kinematic analysis of the drinking task (DRINK) has been recommended to assess the quality of upper limb (UL) movement after stroke, but the accomplishment of this task may become difficult for poststroke patients with hand impairment. Therefore, it is necessary to study ADLs that involve a simpler interaction with a daily life target, such as the turning on a light task (LIGHT). As the knowledge of movement performed by healthy adults becomes essential to assess the quality of movement of poststroke patients, the main goal of this article was to compare the kinematic strategies used by healthy adults in LIGHT with those that are used in DRINK. Methods: 63 adults, aged 30 to 69 years old, drank water and turned on a light, using both ULs separately, while seated. The movements of both tasks were captured by a 3D motion capture system. End-point and joint kinematics of reaching and returning phases were analysed. A multifactorial analysis of variance with repeated measures was applied to the kinematic metrics, using age, sex, body mass index and dominance as main factors. Results: Mean and peak velocities, index of curvature, shoulder flexion and elbow extension were lower in LIGHT, which suggests that the real hand trajectory was smaller in this task. In LIGHT, reaching was less smooth and returning was smoother than DRINK. The instant of peak velocity was similar in both tasks. There was a minimal anterior trunk displacement in LIGHT, and a greater anterior trunk displacement in DRINK. Age and sex were the main factors which exerted effect on some of the kinematics, especially in LIGHT. Conclusion: The different target formats and hand contact in DRINK and LIGHT seem to be responsible for differences in velocity profile, efficiency, smoothness, joint angles and trunk displacement. Results suggest that the real hand trajectory was smaller in LIGHT and that interaction with the switch seems to be less demanding than with the glass. Accordingly, LIGHT could be a good option for the assessment of poststroke patients without grasping ability. Age and sex seem to be the main factors to be considered in future studies for a better match between healthy and poststroke adults.

Facts & Figures

8Proceedings in indexed conferences

2020

6Academic Staff

2020

25Researchers

2016

Contacts