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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 ~40 researchers. Prof. Cunha is also affiliated with 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 was affiliated with Carnegie Mellon – Silicon Valley Campus, NASA Ames Research Park, Mountain View, CA, USA from August 2016 to July 2021 (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), member of the Editorial Board of NATURE/Scientific Reports (6th most cited journal in 2020) and Associate Editor of FRONTIERS/Signal Processing journal, among other scientific editorial memberships. He is also habitual reviewer of several IEEE journals and other relevant scientific journals such as PLoS ONE or Movement Disorders. He has supervised and co-supervised more than 15 PhD & Post-doc students in his areas of R&D. He received several awards, being the most relevant the European Epilepsy Academy (EUREPA) “Best Contribution for Clinical Epileptology” Award in 2002. He mentored, co-founded and contributed to several startups by advising and licensing intellectual property of innovative biomedical technology developed for several years in his lab, such as Biodevices (http://www.biodevices.pt), iLof-Intelligent Lab-on-Fiber (https://ilof.tech) and inSignals Neurotech (http://www.insignals-neurotech.com). Prof. Cunha is co-author of +250 scientific publications and 10 patents (http://shorturl.at/kyGNP), holding an h-index of 33 (Google Scholar), with +4,000 citations. According to Google Scholar, prof. Cunha is currently the 4th most cited author in "Human Motion Analysis" (https://tinyurl.com/ye27whvj), 6th in “Biomedical Sensors” (https://tinyurl.com/pcz9js2e), 10th in "Wearable Devices" (https://tinyurl.com/25r64d4d) and 10th in "Biosignal Processing" (https://tinyurl.com/53ep3nz4)

Details

Details

  • Name

    João Paulo Cunha
  • Role

    Centre Coordinator
  • Since

    01st January 2013
024
Publications

2024

Assessing the perceptual equivalence of a firefighting training exercise across virtual and real environments

Authors
Narciso, D; Melo, M; Rodrigues, S; Dias, D; Cunha, J; Vasconcelos-Raposo, J; Bessa, M;

Publication
VIRTUAL REALITY

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

2023

Studying the Influence of Multisensory Stimuli on a Firefighting Training Virtual Environment

Authors
Narciso, D; Melo, M; Rodrigues, S; Cunha, JP; Vasconcelos-Raposo, J; Bessa, M;

Publication
IEEE Transactions on Visualization and Computer Graphics

Abstract

2023

Using Heart Rate Variability for Comparing the Effectiveness of Virtual vs Real Training Environments for Firefighters

Authors
Narciso, D; Melo, M; Rodrigues, S; Cunha, JP; Vasconcelos-Raposo, J; Bessa, M;

Publication
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Abstract
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants' Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before - 4 after - 7, p = .003; RE: before - 4 after - 5, p = .375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.

2023

BlanketSet - A Clinical Real-World In-Bed Action Recognition and Qualitative Semi-Synchronised Motion Capture Dataset

Authors
Carmona, J; Karacsony, T; Cunha, JPS;

Publication
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IRD action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at rdm.inesctec.pt/dataset/nis-2022-004.

2023

BlanketGen - A Synthetic Blanket Occlusion Augmentation Pipeline for Motion Capture Datasets

Authors
Carmona, J; Karacsony, T; Cunha, JPS;

Publication
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Human motion analysis has seen drastic improvements recently, however, due to the lack of representative datasets, for clinical in-bed scenarios it is still lagging behind. To address this issue, we implemented BlanketGen, a pipeline that augments videos with synthetic blanket occlusions. With this pipeline, we generated an augmented version of the pose estimation dataset 3DPW called BlanketGen3DPW. We then used this new dataset to fine-tune a Deep Learning model to improve its performance in these scenarios with promising results. Code and further information are available at https://gitlab.inesctec.pt/brain-lab/brainlab-public/blanket-gen-releases.

Supervised
thesis

2022

Gait Analysis in Hereditary Amyloidosis Associated to Variant Transthyretin

Author
Maria do Carmo Sousa Cardoso Vilas Boas de Olazabal

Institution
UP-FEUP

2022

Self-Explanatory Deep Learning Models with Concept-based Multimodal Explanations for Medical Imaging Diagnosis

Author
Cristiano Pires Patrício

Institution
UBI-FE

2022

New Methods and Applications for Invisible Electrocardiography (ECG)

Author
Aline dos Santos Silva

Institution
UP-FEUP

2022

Forecasting for Solar Power Farms

Author
Tiago Mourão Pires

Institution
UP-FEP

2022

bio-signal analysis for neuromuscular control assessment: application to the stretch-shortening cycle in the human locomotion system

Author
Carlos Manuel Barbosa Rodrigues

Institution
UP-FEUP