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About

About

Areas of research:

- Knowledge discovery

  • Supervised learning   
  • Multiple predictive models
  • Applied knowledge discovery

- Intelligent transportation systems

  • Planning and operations of public transports

Interest
Topics
Details

Details

  • Name

    João Mendes Moreira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2011
002
Publications

2019

Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, PACBB 2018, Toledo, Spain, 20-22 May, 2018.

Abstract

2019

Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

Authors
Gomes, D; Mendes Moreira, J; Sousa, I; Silva, J;

Publication
Sensors

Abstract

2019

Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

Authors
Gomes, D; Mendes Moreira, J; Sousa, I; Silva, J;

Publication
SENSORS

Abstract
The increasingly aging society in developed countries has raised attention to the role of technology in seniors' lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance.

2019

Automatic Switching Between Video and Audio According to User’s Context

Authors
Ferreira, PJS; Cardoso, JMP; Moreira, JM;

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

Abstract
Smartphones are increasingly present in human’s life. For example, for entertainment many people use their smartphones to watch videos or listen to music. Many users, however, stream or play videos with the intention to only listen to the audio track. This way, some battery energy, which is critical to most users, is unnecessarily consumed thus and switching between video and audio can increase the time of use of the smartphone between battery recharges. In this paper, we present a first approach that, based on the user context, can automatically switch between video and audio. A supervised learning approach is used along with the classifiers K-Nearest Neighbors, Hoeffding Trees and Naive Bayes, individually and combined to create an ensemble classifier. We investigate the accuracy for recognizing the context of the user and the overhead that this system can have on the smartphone energy consumption. We evaluate our approach with several usage scenarios and an average accuracy of 88.40% was obtained for the ensemble classifier. However, the actual overhead of the system on the smartphone energy consumption highlights the need for researching further optimizations and techniques. © 2019, Springer Nature Switzerland AG.

2019

An Efficient Scheme for Prototyping kNN in the Context of Real-Time Human Activity Recognition

Authors
Ferreira, PJS; Magalhães, RMC; Garcia, KD; Cardoso, JMP; Moreira, JM;

Publication
Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part I

Abstract

Supervised
thesis

2018

Safety features for unmanned maritime vehicles

Author
André Manuel Matos Leite

Institution
UP-FEUP

2018

Antenna Design for Underwater Applications

Author
Oluyomi Aboderin

Institution
INESCTEC

2018

Efeitos da Integração Óptima de Sistemas de Armazenamento de Energia na Operação da Rede de Transmissão Portuguesa

Author
André Filipe Porelo da Silva

Institution
UP-FEUP

2017

Analytics Application for Real Time Stream Data Processing from Connected Heating Systems

Author
Manuel Duarte Vilarinho Mourato

Institution
UP-FEUP

2017

On robust bus schedulling

Author
Yassine Baghoussi

Institution
UP-FCUP