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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

Seizure Journal highlights INESC TEC article in the area of epilepsy

João Paulo Cunha and Hugo Miguel Choupina, coordinator and researcher, respectively, of INESC TEC'S Centre for Biomedical Engineering Research (C-BER) are part of the team of authors of the most relevant article of August for the Seizure Journal, a reference in the epilepsy area.

21st September 2018

Networked Intelligent Systems

INESC TEC organises conference on image analysis and recognition

ICIAR 2018 is an international conference on Image Analysis and Recognition organised by INESC TEC's Centre for Biomedical Engineering Research (C-BER). It took place on 27 and 29 June in Póvoa de Varzim.

25th July 2018

Networked Intelligent Systems

International magazine on medical image analysis recognises INESC TEC’s researcher

Aurélio Campilho, coordinator and researcher of INESC TEC’s Centre for Biomedical Engineering Research (C-BER) was awarded with the honour «Outstanding Contribution in Reviewing» by the «Medical Image Analysis» magazine.

18th July 2018

Networked Intelligent Systems

INESC TEC awarded with honourable mentions of the “The best of the technological Portugal” prize

The projects VR2Market and MarinEye received two honourable mentions in the Innovation and Sustainability category of the “The Best of the Technological Portugal” prize, awarded by the Portuguese magazine “Exame Informática”.

02nd July 2018

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

Interest Topics
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Featured Projects

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

2015-2018

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2018

SMILES

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

2015-2018

VR2Market

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

2014-2018

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
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Laboratories

Biomedical Imaging Laboratory

Neuroengineering and Advanced Human Sensing Laboratory

BioInstrumentation Lab

Publications

C-BER Publications

View all Publications

2018

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

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

Publication
Biochimica et Biophysica Acta (BBA) - General Subjects

Abstract

2018

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

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

Publication
Sensors

Abstract

2018

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

Authors
Seixas, A; Vilas Boas, MD; Carvalho, R; Coelho, T; Ammer, K; Vilas Boas, JP; Vardasca, R; Silva Cunha, JPS; Mendes, J;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
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.

2018

Quantification of gait parameters with inertial sensors and inverse kinematics

Authors
Boetzel, K; Olivares, A; Cunha, JP; Gorriz Saez, JMG; Weiss, R; Plate, A;

Publication
Journal of Biomechanics

Abstract
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

2018

NeuroKinect 3.0: Multi-bed 3Dvideo-EEG system for epilepsy clinical motion monitoring

Authors
Choupina, HMP; Rocha, AP; Fernandes, JM; Vollmar, C; Noachtar, S; Cunha, JPS;

Publication
Studies in Health Technology and Informatics

Abstract
Epilepsy diagnosis is typically performed through 2Dvideo-EEG monitoring, relying on the viewer's subjective interpretation of the patient's movements of interest. Several attempts at quantifying seizure movements have been performed in the past using 2D marker-based approaches, which have several drawbacks for the clinical routine (e.g. occlusions, lack of precision, and discomfort for the patient). These drawbacks are overcome with a 3D markerless approach. Recently, we published the development of a single-bed 3Dvideo-EEG system using a single RGB-D camera (Kinect v1). In this contribution, we describe how we expanded the previous single-bed system to a multi-bed departmental one that has been managing 6.61 Terabytes per day since March 2016. Our unique dataset collected so far includes 2.13 Terabytes of multimedia data, corresponding to 278 3Dvideo-EEG seizures from 111 patients. To the best of the authors' knowledge, this system is unique and has the potential of being spread to multiple EMUs around the world for the benefit of a greater number of patients. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.

Facts & Figures

18Proceedings in indexed conferences

2016

11PhDs

2016

0EU Programmes (k€)

2016

Contacts