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Sobre

Sobre

Nuno Feixa Rodrigues é Professor Coordenador na área de Ciência e Tecnologia da Programação na Escola Superior de Tecnologia do Instituto Politécnico do Cávado e Ave (EST-IPCA). Entre 2019 e 2021, assumiu o cargo de Vogal do Conselho Diretivo da Fundação para a Ciência e a Tecnologia (FCT) e de Coordenador Geral da Iniciativa Nacional para as Competências Digitais (INCoDe.2030). Doutorado em Engenharia Informática pela Universidade do Minho, Nuno Rodrigues foi o primeiro diretor de curso da Licenciatura em Engenharia de Desenvolvimento de Jogos Digitais no IPCA. Coordenou e participou em diferentes projetos de investigação na área da programação e das tecnologias digitais, tendo publicado mais de 50 artigos científicos e participado em diversos comités científicos internacionais. Durante os anos 2009 e 2010 realizou um pós-doutoramento sobre métodos de programação de suporte à validação de software criptográfico no projeto Europeu CACE (Computer Aided Cryptography Engineering). Foi diretor da EST-IPCA e do DIGARC – Digital Games Research Center, do IPCA, entre 2011 e 2017. Coordenou projetos digitais de interesse estratégico, como a Estratégia Nacional de Computação Avançada e é o representante português para o Blockchain Expert Policy Advisory Board, da OCDE, e para o Digital Single Market Strategic Group, da Comissão Europeia. Faz ainda parte do grupo de peritos ONE AI da OCDE.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Nuno Feixa Rodrigues
  • Cargo

    Investigador Sénior
  • Desde

    01 novembro 2011
002
Publicações

2023

Current devices and Future Perspectives on Neuromuscular Blockade Monitoring: A Systematic Review

Autores
Torneiro, A; Oliveira, E; Rodrigues, NF;

Publicação
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH

Abstract
Postoperative residual neuromuscular block (PRNB) is still a problem during the surgery procedures resulting in health problems, such as, airway obstruction, hypoxia and pulmonary aspiration. To perform more accurate monitoring of the patient during surgery quantitative neuromuscular blockade monitoring measuring TOF ratio has been recommended by medical institutions. There are some devices available using different techniques, however there are only a few number of clinicians using them, since those devices are costly and have difficult clinical set-up. This paper presents a systematic review of current devices for quantitative neuromuscular monitoring during the surgery procedure following the PRISMA methodology. This study was carried out to list the currently available devices and report the capabilities that are missing in these devices since 2017. The databases used to do the research were PubMed, Cochrane Library, PubMed Central (PMC), Web of Science, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ). 17 articles were selected, presenting comparisons between two devices using different techniques. Quantitative monitoring provides the most accurate TOF ratio measurement but still needs to be incentivized.

2023

The Effects And Viability Of Video Games On The Rehabilitation Of Schizophrenic Patients: A Systematic Review

Autores
Pinto, G; Barroso, B; Rodrigues, N; Guimaraes, M; Oliveira, E;

Publicação
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH

Abstract
Background: Schizophrenia is the most common psychotic illness in the world. The negative and cognitive symptoms of this mental disorder often prevent full reintegration of patients into society, and cannot be effectively addressed with drugs alone, relying on therapy and rehabilitation. Video games as a digital tool for rehabilitation and therapy can help promote accessibility, improve patient engagement and reduce costs to institutions. Methods: A systematic review was conducted from October to November 2022 to analyze the effects of video game based rehabilitation and therapy on negative and cognitive symptoms in schizophrenic patients. The databases used to perform the search were Scopus, PubMed and Web of Science, with the search query: Schizophrenia AND (Video Game OR Serious Game). Results: A total of 228 papers were found, of which 88 duplicates were removed. After reading the titles and abstracts of the remaining 140 papers, 116 were excluded for not meeting the defined eligibility criteria for the review. Of the 24 papers left, 20 were excluded for similar reasons, resulting in the inclusion of four studies in this systematic review Conclusion: The available data for this review was limited, highlighting a need for more research in the field as well as standardization of terms used to describe the digital tools developed and assessment methods used to gather results from these interventions. Nevertheless, statistical data from the four studies included in this review showed that serious games are a promising tool for the rehabilitation and therapy of negative and cognitive symptoms of schizophrenic patients, with significant effects on the patients' performance and motivation.

2023

Future perspectives of deep learning in laparoscopic tool detection, classification, and segmentation: a systematic review

Autores
Fernandes, N; Oliveira, E; Rodrigues, NF;

Publicação
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH

Abstract
Background-Classification, detection, and segmentation of minimally invasive instruments is an essential component for robotic-assisted surgeries and surgical skill assessments. Methods-Cochrane Library, PubMed, ScienceDirect, and IEEE Xplore databases were searched from January 2018 to May 2022. Selected studies evaluated deep learning (DL) models for image and video analysis of laparoscopic surgery. Comparisons were made of the studies' characteristics such as the dataset source, type of laparoscopic operation, number of images/videos, and types of neural networks (NN) used. Results-22 out of 152 studies identified met the selection criteria. The application with the greatest number of studies was instrument detection (59.1%) and the second was instrument segmentation (40.9%). The most tested procedure was cholecystectomy (72.73%). Conclusions-Although CNN-based algorithms outperform other methods in instrument detection and many have been proposed, there are still challenging conditions where numerous difficulties arise. U-Nets are the dominant force in the field for segmentation, but other models such as Mask R-CNN follow close behind with comparable results. Deep learning holds immense potential in laparoscopic surgery and many improvements are expected as soon as data quality improves.

2023

Review apps to evaluate stroke risk in prehospital setting

Autores
Oliveira, E; Ferreira, J; Alves, J; Henriques, M; Rodrigues, NF;

Publicação
2023 IEEE 11TH INTERNATIONAL CONFERENCE ON SERIOUS GAMES AND APPLICATIONS FOR HEALTH, SEGAH

Abstract
Mobile applications have experienced exponential growth in recent years, including mHealth apps related to stroke, one of the most prevalent diseases worldwide. This review aims to analyze the characteristics of available stroke apps designed to assist in assessing stroke severity. Initially, 809 apps were retrieved from both the App Store and Google Play Store. These apps were then filtered, primarily excluding those that did not implement a prehospital stroke scale with a resulting score. A total of 36 apps met the criteria for further analysis in this review. The majority of these apps displayed scale items using text only. Certain scales, such as RACE, VAN, and NIHSS, are supported by studies demonstrating their ability to accurately assess stroke severity. Consequently, apps featuring these scales are more likely to be useful in achieving the objective of this study. Improvements to these apps could be made by expanding the functionalities they offer and enhancing their user experience.

2022

Designing a Music-Based Game for Training Pattern Recognition Control of a Myoelectric Prosthesis

Autores
Bessa, D; Rodrigues, NF; Oliveira, E; Kolbenschlag, J; Prahm, C;

Publicação
Biosystems and Biorobotics

Abstract
Access to early myoelectric training can be a crucial step in mastering prosthesis control. Controlling a prothesis is a cognitively demanding task with high rejection rates. Serious games not only provide patients with an opportunity to train their myoelectric control, but also help maintain their engagement throughout the extensive rehabilitation process. This work proposes a novel serious game design to train machine learning based myoelectric control, implemented in the form of a music-based app. The prototype of the game was evaluated by seven able-bodied participants and three clinical professionals with regard to system usability and motivation. Results showed positive outcomes in motivation, and a need for specific system usability improvements. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.