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About

Associate Professor with Habilitation at University of Trás-os-Montes e Alto Douro (UTAD) and Senior Researcher at INESC TEC.

He earned a doctorate in UTAD in 2002 in Electrical Engineering and held in 2008 the Habilitation in Informatics/Accessibility. I was Associate Professor in December 2012.

He was Pro-Rector for Innovation and Information Management at UTAD, from 23 July 2010 to 29 July 2013.

He produced over 150 scientific papers, including book chapters, journal articles and articles in proceedings of scientific events. He supervised 40 postgraduate students (masters and doctorates).
He was member of the research team in 35 research and development projects.

He was member of several organizing committees of the international scientific meetings. In 2006 he directed the team that created the conference "Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (www.dsai.ws/2016) and in 2016 the conference Technology and Innovation is Sports, Health and Wellbeing (www.tishw.ws/2016).
The main research interests are: Digital Image Processing, Accessibility and Human Computer Interaction.

Google Scholar: http://scholar.google.com/citations?user=HBVvNYQAAAAJ&hl=en

SCOPUS: http://www.scopus.com/authid/detail.url?authorId=20435746800

Interest
Topics
Details

Details

  • Name

    João Barroso
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
007
Publications

2020

System to detect and approach humans from an aerial view for the landing phase in a UAV delivery service

Authors
Safadinho, D; Ramos, J; Ribeiro, R; Filipe, V; Barroso, J; Pereira, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
The possibility to engage in autonomous flight through geolocation-based missions turns Unmanned Aerial Vehicles (UAV) into valuable tools that save time and resources in services like deliveries and surveillance. Amazon is already developing a drop-by delivery service, but there are limitations regarding the client’s id, that can be analyzed in three phases: the approach to the potential receiver, the authorization through the client id and the delivery itself. This work shows a solution for the first of these phases. Firstly, the receiver identifies the GPS coordinates where he wants to receive the package. The UAV flights to that place and tries to locate the receiver on the arrival through Computer Vision (CV) techniques, more precisely Deep Neural Networks (DNN), to continue to the next phase, the identification. After the proposal of the system’s architecture and the prototype’s implementation, a test scenario to analyze the feasibility of the proposed techniques was created. The results were quite good considering a system to look for one person in a limited area defined by the destination coordinates, confirming the detection of one person with an up to 92% accuracy from a 10 m height and 5 m horizontal distance in low resolution images. © Springer Nature Switzerland AG 2020.

2020

UAV Landing Using Computer Vision Techniques for Human Detection

Authors
Safadinho, D; Ramos, J; Ribeiro, R; Filipe, V; Barroso, J; Pereira, A;

Publication
SENSORS

Abstract
The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

2019

Submitted to the WorldCIST'17: The AppVox mobile application, a tool for speech and language training sessions

Authors
Rocha, T; Goncalves, C; Fernandes, H; Reis, A; Barroso, J;

Publication
Expert Systems

Abstract
AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness); number of errors made, as well as number and type of difficulties found (efficiency); and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface. © 2019 John Wiley & Sons, Ltd

2019

A review of assistive spatial orientation and navigation technologies for the visually impaired

Authors
Fernandes, H; Costa, P; Filipe, V; Paredes, H; Barroso, J;

Publication
Universal Access in the Information Society

Abstract
The overall objective of this work is to review the assistive technologies that have been proposed by researchers in recent years to address the limitations in user mobility posed by visual impairment. This work presents an “umbrella review.” Visually impaired people often want more than just information about their location and often need to relate their current location to the features existing in the surrounding environment. Extensive research has been dedicated into building assistive systems. Assistive systems for human navigation, in general, aim to allow their users to safely and efficiently navigate in unfamiliar environments by dynamically planning the path based on the user’s location, respecting the constraints posed by their special needs. Modern mobile assistive technologies are becoming more discrete and include a wide range of mobile computerized devices, including ubiquitous technologies such as mobile phones. Technology can be used to determine the user’s location, his relation to the surroundings (context), generate navigation instructions and deliver all this information to the blind user. © 2017 Springer-Verlag GmbH Germany

2019

Classification of Physical Exercise Intensity Based on Facial Expression Using Deep Neural Network

Authors
Khanal, SR; Sampaio, J; Barroso, J; Filipe, V;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
If done properly, physical exercise can help maintain fitness and health. The benefits of physical exercise could be increased with real time monitoring by measuring physical exercise intensity, which refers to how hard it is for a person to perform a specific task. This parameter can be estimated using various sensors, including contactless technology. Physical exercise intensity is usually synchronous to heart rate; therefore, if we measure heart rate, we can define a particular level of physical exercise. In this paper, we proposed a Convolutional Neural Network (CNN) to classify physical exercise intensity based on the analysis of facial images extracted from a video collected during sub-maximal exercises in a stationary bicycle, according to standard protocol. The time slots of the video used to extract the frames were determined by heart rate. We tested different CNN models using as input parameters the individual color components and grayscale images. The experiments were carried out separately with various numbers of classes. The ground truth level for each class was defined by the heart rate. The dataset was prepared to classify the physical exercise intensity into two, three, and four classes. For each color model a CNN was trained and tested. The model performance was presented using confusion matrix as metrics for each case. The most significant color channel in terms of accuracy was Green. The average model accuracy was 100%, 99% and 96%, for two, three and four classes classification, respectively. © 2019, Springer Nature Switzerland AG.

Supervised
thesis

2019

Internet of Things 4 Seniors

Author
Gonçalo Rijo

Institution
UTAD

2019

Sistema de processamento e visualização de dados de atletas

Author
Nuno Miguel Ferreira Cerdeira Lopes

Institution
UTAD

2019

Um modelo para a capacitação individual através da personalização intrinseca de tarefas de crowdsourcing

Author
Dennis Lourenço Paulino

Institution
UTAD

2019

Plataforma de Controlo e Serviços de Veículos não Tripulados

Author
João Ramos

Institution
UTAD

2019

Acessibilidade dos conteúdos educacionais online na perspetiva da experiência do aluno cego

Author
Isolda Veronese Moniz Vianna Lisboa

Institution
UTAD