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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

INESC TEC once again participates in another edition of the Mostra of the University of Porto

«Dont’ give up on you!» This was the slogan chosen by the University of Porto to lead the 16th edition of Mostra of the University of Porto that took place between 12 and 15 of April at the Institute of Biomedical Sciences Abel Salazar/ the Faculty of Pharmacy of U.Porto.

01st May 2018

World Intellectual Property Day highlights women

Every April 26, we celebrate World Intellectual Property Day to learn about the role that intellectual property rights (patents, trademarks, industrial designs, copyright) play in encouraging innovation and creativity.

27th April 2018

INESC TEC obtains the first patents as sole owner in the US and South Korea

The C4MiR technology - Control Module for Multiple Mixed-signal Resources Management result in the first patent family whose applications were granted in USA (US9921835B2) and South Korea (KR10-1842540).

21st April 2018

CoLAB ForestWISE officially created and coordinated by INESC TEC

On 16 March, the Collaborative Laboratory (CoLAB) ForestWISE, coordinated by INESC TEC, was presented publicly with the signature of a memorandum of understanding with the Foundation for Science and Technology (FCT) at the Forest Ciência Viva Centre in Proença-a-Nova.

11th April 2018

From the idea to the patent

The enhancement and transfer of technology have been one of INESC TEC’s intervention priorities over the last few years. The establishment of the INESC TEC’s Technology Licensing Office (SAL) in 2014 was crucial to the real progress in the protection of the intellectual property created in the institute.

28th March 2018

Interest Topics

Featured Projects


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


Optical Fiber Tips for Biological Applications: from Light Confinement, Biosensing to Bioparticles Manipulation

Paiva, JS; Jorge, PAS; Rosa, CC; Cunha, JPS;

Biochimica et Biophysica Acta (BBA) - General Subjects



Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Paiva, JS; Ribeiro, RSR; Cunha, JPS; Rosa, CC; Jorge, PAS;




Skin temperature of the foot: A comparative study between familial amyloid polyneuropathy and diabetic foot patients

Seixas, A; do Carmo Vilas Boas, M; Carvalho, R; Coelho, T; Ammer, K; Vilas Boas, JP; Vardasca, R; Cunha, JPS; Mendes, J;

Lecture Notes in Computational Vision and Biomechanics

Skin temperature regulation is dependant of the autonomic nervous system function, which may be impaired in patients with neuropathy. Studies reporting thermographic assessment of patients with established diagnosis of Diabetic Foot (DF) are scarce but this information is completely absent in patients suffering from Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). The aim of this study is to compare skin temperature distribution in patients with DF and TTR-FAP. Thermograms of the dorsal and plantar surfaces were compared. Skin temperature was higher in the diabetic foot group and differences were statistically significant (p < 0.05) in both regions of interest. © 2018, Springer International Publishing AG.


Quantification of gait parameters with inertial sensors and inverse kinematics

Bötzel, K; Olivares, A; Cunha, JP; Górriz Sáez, JM; Weiss, R; Plate, A;

Journal of Biomechanics

Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson's disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill. Results: We found the precision of the Kalman filtered angular position is in the range of 2–6° (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed. © 2018 Elsevier Ltd


Retinal image quality assessment by mean-subtracted contrast-normalized coefficients

Galdran, A; Araújo, T; Mendonça, AM; Campilho, A;

Lecture Notes in Computational Vision and Biomechanics

The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost. © 2018, Springer International Publishing AG.

Facts & Figures

1R&D Employees


7Senior Researchers


6Academic Staff