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Publications

Publications by C-BER

2020

Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

Authors
Martins, M; Gomes, P; Oliveira, C; Coimbra, M; da Silva, HP;

Publication
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Abstract
Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure.

2020

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Authors
Pires, IM; Marques, G; Garcia, NM; Florez Revuelta, F; Canavarro Teixeira, M; Zdravevski, E; Spinsante, S; Coimbra, M;

Publication
ELECTRONICS

Abstract
The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs' identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

2020

Gaussian Mixture Model Based Probabilistic Modeling of Images for Medical Image Segmentation

Authors
Riaz, F; Rehman, S; Azad, MA; Hafiz, R; Hassan, A; Aljohani, NR; Nawaz, R; Young, RCD; Coimbra, MT;

Publication
IEEE Access

Abstract
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. © 2013 IEEE.

2020

Secure Triplet Loss for End-to-End Deep Biometrics

Authors
Pinto, JR; Cardoso, JS; Correia, MV;

Publication
2020 8th International Workshop on Biometrics and Forensics (IWBF)

Abstract

2020

Comparison of upper limb kinematics in two activities of daily living with different handling requirements

Authors
Mesquita, IA; Pereira da Fonseca, PFP; Borgonovo Santos, M; Ribeiro, E; Vieira Pinheiro, ARV; Correia, MV; Silva, C;

Publication
HUMAN MOVEMENT SCIENCE

Abstract
Introduction: Recently, kinematic analysis of the drinking task (DRINK) has been recommended to assess the quality of upper limb (UL) movement after stroke, but the accomplishment of this task may become difficult for poststroke patients with hand impairment. Therefore, it is necessary to study ADLs that involve a simpler interaction with a daily life target, such as the turning on a light task (LIGHT). As the knowledge of movement performed by healthy adults becomes essential to assess the quality of movement of poststroke patients, the main goal of this article was to compare the kinematic strategies used by healthy adults in LIGHT with those that are used in DRINK. Methods: 63 adults, aged 30 to 69 years old, drank water and turned on a light, using both ULs separately, while seated. The movements of both tasks were captured by a 3D motion capture system. End-point and joint kinematics of reaching and returning phases were analysed. A multifactorial analysis of variance with repeated measures was applied to the kinematic metrics, using age, sex, body mass index and dominance as main factors. Results: Mean and peak velocities, index of curvature, shoulder flexion and elbow extension were lower in LIGHT, which suggests that the real hand trajectory was smaller in this task. In LIGHT, reaching was less smooth and returning was smoother than DRINK. The instant of peak velocity was similar in both tasks. There was a minimal anterior trunk displacement in LIGHT, and a greater anterior trunk displacement in DRINK. Age and sex were the main factors which exerted effect on some of the kinematics, especially in LIGHT. Conclusion: The different target formats and hand contact in DRINK and LIGHT seem to be responsible for differences in velocity profile, efficiency, smoothness, joint angles and trunk displacement. Results suggest that the real hand trajectory was smaller in LIGHT and that interaction with the switch seems to be less demanding than with the glass. Accordingly, LIGHT could be a good option for the assessment of poststroke patients without grasping ability. Age and sex seem to be the main factors to be considered in future studies for a better match between healthy and poststroke adults.

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.

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