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Sobre

Áreas de investigação:

- Descoberta de conhecimento

  • Aprendizagem supervisionada   
  • Modelos múltiplos preditivos
  • Descoberta de conhecimento aplicada

- Sistemas inteligentes de transportes

  • Planeamento e operações de transportes públicos

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Mendes Moreira
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2011
002
Publicações

2019

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

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

Publicação
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

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

Publicação
Sensors

Abstract

2019

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

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

Publicação
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

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

Publicação
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

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

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

Abstract

Teses
supervisionadas

2018

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

Autor
André Filipe Porelo da Silva

Instituição
UP-FEUP

2018

Safety features for unmanned maritime vehicles

Autor
André Manuel Matos Leite

Instituição
UP-FEUP

2018

Antenna Design for Underwater Applications

Autor
Oluyomi Aboderin

Instituição
INESCTEC

2017

Novas perspetivas do Business Intelligence: criação de novos indicadores de avaliação

Autor
Tânia Patrícia Serra Veloso

Instituição
UP-FEP

2017

Leveraging Metalearning for Bagging Classifiers

Autor
Fábio Hernâni dos Santos Costa Pinto

Instituição
UP-FEUP