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About

About

I hold an integrated masters (bachelor+master) degree in Bioengineering, branch of Biomedical Engineering, focused on a wide array of subjects related to healthcare and engineering, such as sensors and electronics, signal and image processing, computer aided diagnosis, medical instruments, robotics and bionics, mobile programming, and software engineering.

I am currently a Research Assistant at CTM - INESC TEC and a PhD student at FEUP, conducting research in signal and image analysis, combined with machine learning for pattern recognition applications. My work focuses mainly on biometrics, for human identification and authentication, using electrocardiographic signals from off-the-person, highly noisy, unconstrained, and seamless acquisition settings.

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Publications

2022

Streamlining Action Recognition in Autonomous Shared Vehicles with an Audiovisual Cascade Strategy

Authors
Pinto, JR; Carvalho, P; Pinto, C; Sousa, A; Capozzi, L; Cardoso, JS;

Publication
PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 5

Abstract

2022

Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Authors
Neto, PCP; Pinto, JR; Boutros, F; Damer, N; Sequeira, AF; Cardoso, JS;

Publication
IEEE ACCESS

Abstract

2022

Towards vehicle occupant-invariant models for activity characterisation

Authors
Capozzi, L; Barbosa, V; Pinto, C; Pinto, JR; Pereira, A; Carvalho, PM; Cardoso, JS;

Publication
IEEE ACCESS

Abstract

2022

Electrocardiogram lead conversion from single-lead blindly-segmented signals

Authors
Beco, SC; Pinto, JR; Cardoso, JS;

Publication
BMC MEDICAL INFORMATICS AND DECISION MAKING

Abstract
Abstract Background The standard configuration’s set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient’s limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks. Methods Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead. Results Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method’s performance. Conclusions This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.

2022

OCFR 2022: Competition on Occluded Face Recognition from Synthetically Generated Structure-Aware Occlusions

Authors
Neto, PC; Boutros, F; Pinto, JR; Damer, N; Sequeira, AF; Cardoso, JS; Bengherabi, M; Bousnat, A; Boucheta, S; Hebbadj, N; Erakin, ME; Demir, U; Ekenel, HK; De Queiroz Vidal, PB; Menotti, D;

Publication
2022 IEEE International Joint Conference on Biometrics (IJCB)

Abstract