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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

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

FCT approved five INESC TEC projects to fight against COVID-19

The Foundation for Science and Technology (FCT) approved five INESC TEC projects under the "RESEARCH FOR COVID-19" financing line, with the Institute as the applicant entity in three of said projects - and a total of €85M funding. These initiatives will focus on the development of solutions to fight against the COVID-19 pandemic.

27th April 2020

Networked Intelligent Systems

INESC TEC develops smart optical technology for the distinction and detection of cancer cells

An article published in one of Nature's scientific journals shows that iLoF (Lab on Fiber) intelligent device is a promising technology to identify cancer cells with different profiles

12th March 2020

Networked Intelligent Systems

«Best Practical Paper Award» for INESC TEC research on retinopathy screening

“EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection” is the title of the scientific article that earned the Best Practical Paper Award at the MVA 2019 (International Conference on Machine Vision Applications) held in Tokyo, Japan.

14th February 2020

Networked Intelligent Systems

INESC TEC has its first patent granted in China

INESC TEC has a patent granted in China for the first time. The accomplishment was achieved with the C4Mir technology ("Control Module for Multiple Mixed-Signal Resources Management"), which had already seen its patent applications be granted in Europe, USA, South Korea and Japan.

30th December 2019

Interest Topics

Featured Projects


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



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



Transparent Artificial Medical Intelligence



Serviços de apoio técnico - iLoF spin-off



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



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



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



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



Perceptual equivalence in virtual reality for authentic training



Less Commodities more Specialities



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



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



Projeto Vital Sticker no âmbito do Contrato Programa



NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis



NanoSTIMA - Macro-to-Nano Human Sensing Technologies



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



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



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



Inteligent Eco Driving and Fleet Management



Human motor re-learning by sensor information fusion



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



Movement Disorders in Autistic Spectrum Disorders



Interactive system for digital content consumers




Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab


C-BER Publications

View all Publications


Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

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


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.


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

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


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).


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

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

IEEE Access

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.


Subject Identification Based on Gait Using a RGB-D Camera

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

Advances in Intelligent Systems and Computing

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.


IHandU: A novel quantitative wrist rigidity evaluation device for deep brain stimulation surgery

Murias Lopes, E; Vilas Boas, MD; Dias, D; Rosas, MJ; Vaz, R; Silva Cunha, JP;

Sensors (Switzerland)

Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson’s disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS’s efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient’s hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at São João Hospital in Portugal. © 2020 by the authors.

Facts & Figures

29Proceedings in indexed conferences


22Papers in indexed journals


4Book Chapters