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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

A Study on Hyperparameter Configuration for Human Activity Recognition

Authors
Garcia, KD; Carvalho, T; Moreira, JM; Cardoso, JMP; de Carvalho, ACPLF;

Publication
14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings

Abstract
Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person. © 2020, Springer Nature Switzerland AG.

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.

2018

Enhancing traffic model of big cities: Network skeleton & reciprocity

Authors
Bhanu, M; Chandra, J; Mendes Moreira, J;

Publication
10th International Conference on Communication Systems & Networks, COMSNETS 2018, Bengaluru, India, January 3-7, 2018

Abstract

Supervised
thesis

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

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

2017

Leveraging Metalearning for Bagging Classifiers

Author
Fábio Hernâni dos Santos Costa Pinto

Institution
UP-FEUP

2017

Eating and drinking recognition for triggering smart reminders

Author
Diana Sousa Gomes

Institution
UP-FEUP