A Framework For Motor Function Characterization in Autism Spectrum Disorder João Ruivo Paulo, Teresa Sousa, João Perdiz, Nicoli Leal, Paulo Menezes, et al. 2023 IEEE 7th Portuguese Meeting on Bioengineering Enbeng 2023, 2023 Action/perception cycles have been described to be impaired in autism spectrum disorder (ASD). This goes beyond typical motor coordination, including core symptoms of autism. However, the neural basis of action understanding and motor function impairment still remains poorly characterized. In this paper, we present a framework for motion-related data collection. Electroencephalography (EEG) is recorded during walking and dancing imitation tasks to allow motor function characterization. We also present the validation of the framework based on the analysis of $\\mu$ frequency band activity on EEG signals from neurotypical individuals. $\\mu$ power modulation over the central EEG channels by action/perception cycles showed to be discriminative of all motion-related tasks tested. Both time-frequency analysis and machine-learning approaches corroborate our results.
A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration Jorge Pereira, Jérôme Mendes, Jorge S. S. Júnior, Carlos Viegas, João Ruivo Paulo Mathematics, 2022 Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.
Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms Jorge Pereira, Jerome Mendes, Jorge S. S. Junior, Carlos Viegas, Joao Ruivo Paulo IECON Proceedings Industrial Electronics Conference, 2022 Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model’s selected parameters.
Spatiotemporal 2D skeleton-based image for dynamic gesture recognition using convolutional neural networks Joao Ruivo Paulo, Luis Garrote, Paulo Peixoto, Urbano J. Nunes 2021 30th IEEE International Conference on Robot and Human Interactive Communication Ro Man 2021, 2021 This paper presents a dynamic gesture recognition approach using a novel spatiotemporal 2D skeleton image representation that can be fed to computationally efficient deep convolutional neural networks, for applications on human-robot interaction. Gestures are a seamless modality of human interaction and represent a potentially natural way to interact with the smart devices around us, like robots. The contribution of this paper is the proposal of a visually interpretable representation of dynamic gestures, which has a two-fold advantage: (i) conveys both spatial and temporal characteristics relying on a technique inspired in computer graphics, (ii) and can be used with simple and efficient architectures of convolutional neural networks. In our representation, a 3D skeleton model is projected to a 2D camera’s point-of-view, preserving spatial relations, and through a sliding window the temporal domain is encoded in a fused image of consecutive frames, through a shading motion effect achieved by manipulating a transparency coefficient. The result is a 2D image that when fed to simple custom-designed convolutional neural networks, it is achieved accurate classification of dynamic gestures. Experimmental reuslts obtained with a purposely captured 6 gesture dataset of 11 subjects, and also 2 public datasets, give evidence of a strong performance of our approach, when compared to other methods.
Cross-Subject Zero Calibration Driver's Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification Joao Ruivo Paulo, Gabriel Pires, Urbano J. Nunes IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021 This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness. Advanced driver assistance systems that could detect mental states which are associated with hazardous situations, such as drowsiness, are of critical importance. EEG signals are used widely for brain-computer interfaces, as well as mental state recognition. However, these systems are still difficult to design due to very low signal-to-noise ratios and cross-subject disparities, requiring individual calibration cycles. To tackle this research domain, here, we explore drowsiness detection based on EEG signals’ spatiotemporal image encoding representations in the form of either recurrence plots or gramian angular fields for deep convolutional neural network (CNN) classification. Results comparing both techniques using a public dataset of 27 subjects show a superior balanced accuracy of up to 75.87% for leave-one-out cross-validation, using both techniques, against works in the literature, demonstrating the possibility to pursue cross-subject zero calibration design.
Novelty Detection for Iterative Learning of MIMO Fuzzy Systems Jorge S. S. Junior, Jerome Mendes, Rui Araujo, Joao Ruivo Paulo, Cristiano Premebida IEEE International Conference on Industrial Informatics Indin, 2021 This paper proposes a methodology for iterative learning of multi-input multi-output (MIMO) fuzzy models focusing on dynamic system identification. The first step of the proposed method is the learning of the antecedent part of the fuzzy system, which is learned iteratively, where fuzzy rules can be added or merged based on the presented novelty detection and similarity criteria defined by a recursive extension of the Gath-Geva clustering algorithm. Then, the consequent part consists in the direct implementation of a non-recursive fuzzy approach that uses global least squares, Observer Kalman Filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA). The proposed method is validated using experimental data from a real quadrotor aerial robot, a nonlinear dynamic system. Using quantitative performance metrics, the proposed method is compared with Hammerstein-Wiener models (H.-W.), nonlinear autoregressive models with exogenous input (NARX), and state-space models using subspace method with time-domain data (N4SID), other MIMO system identification techniques. The proposed method achieved better results compared to other techniques, showing the importance and versatility of learning based on novelty detection for MIMO problems.
Classification of reaching and gripping gestures for safety on walking aids J. Paulo, P. Peixoto IEEE Ro Man 2014 23rd IEEE International Symposium on Robot and Human Interactive Communication Human Robot Co Existence Adaptive Interfaces and Systems for Daily Life Therapy Assistance and Socially Engaging Interactions, 2014
Motion Analysis in Autism: Quantification and Classification of Dancing and Walking Tasks L Pereira, T Sousa, R Vigário, M Castelo-Branco, JR Paulo 2025 IEEE 8th Portuguese Meeting on Bioengineering (ENBENG), 101-104 , 2025 2025
A multimodal dataset addressing motor function in autism JR Paulo, T Sousa, J Perdiz, L Pereira, M Vasen, S Mouga, G Pires, ... Scientific data 12 (1), 959 , 2025 2025 Citations: 4
Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo Engineering Applications of Artificial Intelligence 136, 108928 , 2024 2024 Citations: 12
A framework for motor function characterization in autism spectrum disorder JR Paulo, T Sousa, J Perdiz, N Leal, P Menezes, T Zhu, G Pires, ... 2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG), 104-107 , 2023 2023 Citations: 2
IMFire - An Intelligent Wildfire Management and Decision Support System Carlos Viegas, João Paulo, Jérôme Mendes, Luis Ribeiro, António Gameiro ... International Wildland Fire Conference, 1 , 2023 2023
Wildfire spread prediction model calibration using metaheuristic algorithms J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo IECON 2022–48th Annual Conference of the IEEE Industrial Electronics Society … , 2022 2022 Citations: 3
Automatic forest fire danger rating calibration: Exploring clustering techniques for regionally customizable fire danger classification JSS Júnior, JR Paulo, J Mendes, D Alves, LM Ribeiro, C Viegas Expert Systems with Applications 193, 116380 , 2022 2022 Citations: 47
A review of genetic algorithm approaches for wildfire spread prediction calibration J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo Mathematics 10 (3), 300 , 2022 2022 Citations: 58
CNN-based approaches for cross-subject classification in motor imagery: From the state-of-the-art to DynamicNet A Zancanaro, G Cisotto, JR Paulo, G Pires, UJ Nunes 2021 IEEE conference on computational intelligence in bioinformatics and … , 2021 2021 Citations: 45
Spatiotemporal 2D skeleton-based image for dynamic gesture recognition using convolutional neural networks JR Paulo, L Garrote, P Peixoto, UJ Nunes 2021 30th IEEE International Conference on Robot & Human Interactive … , 2021 2021 Citations: 3
Novelty detection for iterative learning of MIMO fuzzy systems JSS Júnior, J Mendes, R Araújo, JR Paulo, C Premebida 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), 1-7 , 2021 2021 Citations: 4
Cross-subject zero calibration driver’s drowsiness detection: Exploring spatiotemporal image encoding of EEG signals for convolutional neural network classification JR Paulo, G Pires, UJ Nunes IEEE transactions on neural systems and rehabilitation engineering 29, 905-915 , 2021 2021 Citations: 96
Proceedings of the 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) A Zancanaro, G Cisotto, JR Paulo, G Pires, UJ Nunes IEEE , 2021 2021 Citations: 7
Machine learning applied to low back pain rehabilitation–a systematic review P Amorim, JR Paulo, PA Silva, P Peixoto, M Castelo-Branco, H Martins International Journal of Digital Health 1 (1), 10 , 2021 2021 Citations: 22
Automatic calibration of forest fire weather index for independent customizable regions based on historical records JSS Júnior, J Paulo, J Mendes, D Alves, LM Ribeiro 2020 IEEE Third International Conference on Artificial Intelligence and … , 2020 2020 Citations: 13
Reinforcement learning aided robot-assisted navigation: A utility and RRT two-stage approach L Garrote, J Paulo, UJ Nunes International Journal of Social Robotics 12 (3), 689-707 , 2020 2020 Citations: 17
Markerless multi-view-based multi-user head tracking system for virtual reality applications D Bicho, P Girão, J Paulo, L Garrote, UJ Nunes, P Peixoto 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC … , 2019 2019 Citations: 9
Multi-view Robust Gesture Recognition for Assistive Interfaces J Paulo, P Girão, P Peixoto Mediterranean Conference on Medical and Biological Engineering and Computing … , 2019 2019
Trajectory-based gait pattern shift detection for assistive robotics applications J Paulo, P Peixoto, P Amorim Intelligent Service Robotics 12 (3), 255-264 , 2019 2019 Citations: 13
Robot-assisted navigation for a robotic walker with aided user intent L Garrote, J Paulo, J Perdiz, P Peixoto, UJ Nunes 2018 27th IEEE international symposium on robot and human interactive … , 2018 2018 Citations: 18
MOST CITED SCHOLAR PUBLICATIONS
Cross-subject zero calibration driver’s drowsiness detection: Exploring spatiotemporal image encoding of EEG signals for convolutional neural network classification JR Paulo, G Pires, UJ Nunes IEEE transactions on neural systems and rehabilitation engineering 29, 905-915 , 2021 2021 Citations: 96
ISR-AIWALKER: Robotic walker for intuitive and safe mobility assistance and gait analysis J Paulo, P Peixoto, UJ Nunes IEEE Transactions on Human-Machine Systems 47 (6), 1110-1122 , 2017 2017 Citations: 96
A review of genetic algorithm approaches for wildfire spread prediction calibration J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo Mathematics 10 (3), 300 , 2022 2022 Citations: 58
Automatic forest fire danger rating calibration: Exploring clustering techniques for regionally customizable fire danger classification JSS Júnior, JR Paulo, J Mendes, D Alves, LM Ribeiro, C Viegas Expert Systems with Applications 193, 116380 , 2022 2022 Citations: 47
CNN-based approaches for cross-subject classification in motor imagery: From the state-of-the-art to DynamicNet A Zancanaro, G Cisotto, JR Paulo, G Pires, UJ Nunes 2021 IEEE conference on computational intelligence in bioinformatics and … , 2021 2021 Citations: 45
3D point cloud downsampling for 2D indoor scene modelling in mobile robotics L Garrote, J Rosa, J Paulo, C Premebida, P Peixoto, UJ Nunes 2017 IEEE international conference on autonomous robot systems and … , 2017 2017 Citations: 24
Machine learning applied to low back pain rehabilitation–a systematic review P Amorim, JR Paulo, PA Silva, P Peixoto, M Castelo-Branco, H Martins International Journal of Digital Health 1 (1), 10 , 2021 2021 Citations: 22
Robot-assisted navigation for a robotic walker with aided user intent L Garrote, J Paulo, J Perdiz, P Peixoto, UJ Nunes 2018 27th IEEE international symposium on robot and human interactive … , 2018 2018 Citations: 18
Reinforcement learning aided robot-assisted navigation: A utility and RRT two-stage approach L Garrote, J Paulo, UJ Nunes International Journal of Social Robotics 12 (3), 689-707 , 2020 2020 Citations: 17
Human gait pattern changes detection system: a multimodal vision-based and novelty detection learning approach J Paulo, A Asvadi, P Peixoto, P Amorim Biocybernetics and Biomedical Engineering 37 (4), 701-717 , 2017 2017 Citations: 16
Automatic calibration of forest fire weather index for independent customizable regions based on historical records JSS Júnior, J Paulo, J Mendes, D Alves, LM Ribeiro 2020 IEEE Third International Conference on Artificial Intelligence and … , 2020 2020 Citations: 13
Trajectory-based gait pattern shift detection for assistive robotics applications J Paulo, P Peixoto, P Amorim Intelligent Service Robotics 12 (3), 255-264 , 2019 2019 Citations: 13
Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model J Pereira, J Mendes, JSS Júnior, C Viegas, JR Paulo Engineering Applications of Artificial Intelligence 136, 108928 , 2024 2024 Citations: 12
An innovative robotic walker for mobility assistance and lower limbs rehabilitation J Paulo, L Garrote, C Premebida, A Asvadi, D Almeida, A Lopes, ... 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), 1-4 , 2017 2017 Citations: 12
ISRobotCar: The autonomous electric vehicle project M Silva, F Moita, U Nunes, L Garrote, H Faria, J Ruivo 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2012 2012 Citations: 12
A novel vision-based human-machine interface for a robotic walker framework J Paulo, P Peixoto, U Nunes 2015 24th IEEE International Symposium on Robot and Human Interactive … , 2015 2015 Citations: 11
Markerless multi-view-based multi-user head tracking system for virtual reality applications D Bicho, P Girão, J Paulo, L Garrote, UJ Nunes, P Peixoto 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC … , 2019 2019 Citations: 9
Proceedings of the 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) A Zancanaro, G Cisotto, JR Paulo, G Pires, UJ Nunes IEEE , 2021 2021 Citations: 7
Classification of reaching and gripping gestures for safety on walking aids J Paulo, P Peixoto The 23rd IEEE International Symposium on Robot and Human Interactive … , 2014 2014 Citations: 6
Real-time multi-view grid map-based spatial representation for mixed reality applications P Girão, J Paulo, L Garrote, P Peixoto International Conference on Augmented Reality, Virtual Reality and Computer … , 2018 2018 Citations: 5
