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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

014
Publications

2020

Virtual reality in training: an experimental study with firefighters

Authors
Narciso, D; Melo, M; Raposo, JV; Cunha, J; Bessa, M;

Publication
Multimedia Tools and Applications

Abstract
Training with Virtual Reality (VR) can bring several benefits, such as the reduction of costs and risks. We present an experimental study that aims to evaluate the effectiveness of a Virtual Environment (VE) to train firefighters using an innovative approach based on a Real Environment (RE) exercise. To measure the VE’s effectiveness we used a Presence Questionnaire (PQ) and participant’s cybersickness, stress and fatigue. Results from the PQ showed that participants rated the VE with high spatial presence and moderate realness and immersion. Signs of stress, analyzed from participant’s Heart-Rate Variability, were shown in the RE but not in the VE. In the remaining variables, there was only an indicative difference for fatigue in the RE. Therefore, the results suggest that although our training VE was successful in giving participants spatial presence and in not causing cybersickness, its realness and immersion provided were not enough to provoke a similar RE response. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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.

2020

iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

Authors
Paiva, JS; Jorge, PAS; Ribeiro, RSR; Balmana, M; Campos, D; Mereiter, S; Jin, CS; Karlsson, NG; Sampaio, P; Reis, CA; Cunha, JPS;

Publication
Scientific reports

Abstract
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.

2020

SnapKi—An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients

Authors
Oliveira, A; Dias, D; Lopes, EM; Vilas Boas, MD; Silva Cunha, JPS;

Publication
Sensors

Abstract
The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.

Supervised
thesis

2019

Man-Machine Symbiosis - EEG and Other Biosignal Man-Machine Interaction

Author
Vítor Daniel Veigas Minhoto

Institution
UP-FEUP

2019

Dispositivos “Low-Cost” para aplicações em espectroscopia de biofluídos

Author
Bruno Albino da Silva Rafael

Institution
UP-FEUP

2019

Towards Personalized In-Silico Mathematical Models and Tools for Brain Networks Simulations and Study of Optimal Therapeutic Approaches for Refractory Epilepsy

Author
Elodie Múrias Lopes

Institution
UP-FEUP

2019

Towards Novel Human Sweat Sensors

Author
Bruno Manuel Carvalho dos Santos

Institution
UP-FEUP

2019

Start-ups - Integrating product, market and supply chain decisions to build-up a market entry strategy

Author
Luís Filipe Carvalho Valente

Institution
UP-FEUP