Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

João Paulo Cunha is Associate Professor (with “Agregação”) at the Department of Electrical and Computer Engineering of the Faculty of Engineering of the University of Porto (FEUP), Portugal and senior researcher at the INESC-TEC: Institute for Systems and Computer Engineering  (http://www.inesctec.pt) where he created the BRAINBiomedical Research And INnovation - research group and co-founded the Center for Biomedical Engineering Research (C-BER) that aggregates ~30 researchers. Prof. Cunha is also affiliated with the Institute for Electronics and Telematics Engineering (IEETA-http://www.ieeta.pt) of the University of Aveiro, Portugal, the Portuguese Brain Imaging Network (http://www.brainimaging.pt ) that he co-founded and co-directed between 2009 and 2012, the Porto Biomechanics Laboratory (http://labiomep.up.pt) and is visiting professor at the Neurology Dep., Faculty of Medicine of the University of Munich (http://www.med.uni-muenchen.de), Bavaria, Germany, since 2002 and at the Carnegie Mellon – Silicon Valley Campus, NASA Ames Research Park, Mountain View, CA, USA since August 2016 (http://www.cmu.edu/silicon-valley/). He presently serves as Scientific Director of the Carnegie-Mellon | Portugal program (http://www.cmuportugal.org) where he is a faculty since 2007, and as the coordinator of the Center of Competencies for the Future Cities of UP (http://futurecities.up.pt).

He earned a degree in Electronics and Telecommunications engineering (1989), a Ph.D. (1996) and an “Agregação” degree (2009) in Electrical Engineering all at the University of Aveiro, Portugal.

Dr. Cunha is Senior Member of the IEEE (2004) where he joined the Engineering in Medicine and Biology Society (EMBS) in 1986 as a student member. He is habitual reviewer of several IEEE journals, such as the IEEE Trans. on Biomedical Eng., IEEE Trans. on Medical Imaging or the IEEE Trans. on Information Technology in Biomedicine. He co-founded in 2007 the spin-off company Biodevices SA (http://www.biodevices.pt) to bring to the market innovative biomedical technology developed for several years in his lab. His R&D activities are focused in Neuro-Engineering and Advanced Human Sensing technologies. Prof. Cunha is co-author of more than 250 scientific publications and 4 patents, holding an h-index of 17, with more than 1000 citations.

Interest
Topics
Details

Details

012
Publications

2020

Subject Identification Based on Gait Using a RGB-D Camera

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

Publication
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.

2020

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

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

Publication
Sensors (Switzerland)

Abstract
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.

2019

Skin temperature of the foot: comparing transthyretin Familial Amyloid Polyneuropathy and Diabetic Foot patients

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

Publication
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Abstract

2019

Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine

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

Publication
International Journal of Nanomedicine

Abstract

2019

Full-body motion assessment: Concurrent validation of two body tracking depth sensors versus a gold standard system during gait

Authors
Vilas Boas, MD; Pereira Choupina, HMP; Rocha, AP; Fernandes, JM; Silva Cunha, JPS;

Publication
Journal of Biomechanics

Abstract
RGB-D cameras provide 3-D body joint data in a low-cost, portable and non-intrusive way, when compared with reference motion capture systems used in laboratory settings. In this contribution, we evaluate the validity of both Microsoft Kinect versions (v1 and v2) for motion analysis against a Qualisys system in a simultaneous protocol. Two different walking directions in relation to the Kinect (towards – WT, and away – WA) were explored. For each gait trial, measures related with all body parts were computed: velocity of all joints, distance between symmetrical joints, and angle at some joints. For each measure, we compared each Kinect version and Qualisys by obtaining the mean true error and mean absolute error, Pearson's correlation coefficient, and optical-to-depth ratio. Although both Kinect v1 and v2 and/or WT and WA data present similar accuracy for some measures, better results were achieved, overall, when using WT data provided by the Kinect v2, especially for velocity measures. Moreover, the velocity and distance presented better results than angle measures. Our results show that both Kinect versions can be an alternative to more expensive systems such as Qualisys, for obtaining distance and velocity measures as well as some angles metrics (namely the knee angles). This conclusion is important towards the off-lab non-intrusive assessment of motor function in different areas, including sports and healthcare. © 2019 Elsevier Ltd

Supervised
thesis

2017

PhD Work Plan: NeuroOptics - Towards novel optical tools for Neuroscience

Author
Joana Isabel dos Santos Paiva

Institution
UP-FEUP

2017

Quantitative assessment of motor performance during robot-aided rehabilitation: preliminary results from NEUROPROBEs project

Author
Débora Marisa Araújo da Silva Pereira

Institution
UP-FEUP

2017

Human Sensing and Indoor Location: From coarse to fine detection algorithms based on consumer electronics RF mapping

Author
Duarte Fleming Oliveira de Sousa

Institution
UP-FEUP

2016

Software Defined Radar for Medical Imaging

Author
Wilson José dos Santos Silva

Institution
UP-FEUP

2016

MobileVJ: A mobile app for a novel wearable human sensing system

Author
Pavel Alexeenko

Institution
UP-FEUP