A carregar...
4 resultados
Resultados da pesquisa
A mostrar 1 - 4 de 4
- Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature ReviewPublication . Lima, Arthur A. J.; Lopes, Júlio Castro; Lopes, Rui Pedro; Figueiredo, Tomás d'Aquino; Vidal-Vásquez, Eva; Hernandez Hernandez, ZulimarIn the current global change scenario, valuable tools for improving soils and increasing both agricultural productivity and food security, together with effective actions to mitigate the impacts of ongoing climate change trends, are priority issues. Soil Organic Carbon (SOC) acts on these two topics, as C is a core element of soil organic matter, an essential driver of soil fertility, and becomes problematic when disposed of in the atmosphere in its gaseous form. Laboratory methods to measure SOC are expensive and time-consuming. This Systematic Literature Review (SLR) aims to identify techniques and alternative ways to estimate SOC using Remote-Sensing (RS) spectral data and computer tools to process this database. This SLR was conducted using Systematic Review and MetaAnalysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. The SLR concludes that Sentinel satellites, particularly Sentinel-2, were frequently used. Despite limited datasets, DL models demonstrated robust performance as assessed by R2 and RMSE. Key input data, such as vegetation indices (e.g., NDVI, SAVI, EVI) and digital elevation models, were consistently correlated with SOC predictions. These findings underscore the potential of combining RS and advanced artificial-intelligence techniques for efficient and scalable SOC monitoring.
- Stress inference in a virtual reality game for rehabilitation with body motion and heart ratePublication . Lopes, Júlio Castro; Van-Deste, Isaac; Vieira, João; Lopes, Rui PedroThis paper proposes an architecture for detecting stress in a player, while playing a Virtual Reality (VR) game, by analyzing the player’s movements as well as the player’s Heart Rate (HR). For this effect, only a camera to analyze players’ Body Motion Rate (BMR) and a smartwatch to capture the HR, were used. As part of the computation of the BMR, computer vision techniques were used to detect the player’s skeleton, computing the difference between frames. A dataset was captured in this paper, while the players tested 5 different scenarios to induce different stress situations. The proposed dataset serves as a proof of concept to validate the relation between HR and BMR. Future work should investigate synthetic data generation techniques to improve dataset diversity and adaptability for Dynamic Difficulty Adjustment (DDA) systems. This research contributes to advancing stress detection in VR, with potential applications in rehabilitation, particularly for conditions such as schizophrenia, promoting improved well-being and stress management in the long run.
- Partial occlusion in facial expression recognition: a systematic literature reviewPublication . Rodrigues, Ana Sofia; Lopes, Júlio Castro; Vieira, João; Lopes, Rui PedroThis study presents a Systematic Literature Review (SLR) examining Facial Expression Recognition (FER) under partial occlusion conditions. Analyzing a diverse array of studies, it identifies prevalent methodologies and challenges within the field. The review reveals a significant use of Gabor filters for feature extraction and classification, highlighting their efficiency in FER research. Additionally, black patches emerge as the primary method for simulating occlusions, which reflects the scarcity of datasets containing such occlusions. The complexity of FER under occlusion conditions is highlighted by the inconclusive findings on the relative importance of facial regions like the mouth and eyes in expression recognition. The results point to the need for more research to comprehend the complex role of different facial regions in conveying emotions. Furthermore, the review emphasizes the importance of developing larger and more diverse datasets, that encompass a broader range of facial expressions and occlusion types, to advance the development and evaluation of FER systems in real-world scenarios.
- User-Centered Dashboard Design in Serious VR Game for Cognitive RehabilitationPublication . Neto, Jecé Xavier; Lopes, Júlio Castro; Naves, Thiago França; Lopes, Rui PedroThe product designer plays a crucial role in creating products that captivate and fulfill users’ needs in an exceptional way. Based on this, this work aimed to demonstrate how a user-centered product designer can play a crucial role in solving the pains of the target audience. With this in mind, this paper proposes the design of a dashboard for a clinical team to visualize a rehabilitation session, focusing the product on the user experience. Several Deep Learning (DL) algorithms are expected to be used to extract biometric data from the patients, in order to feed a Dynamic Difficulty Adjustment (DDA) module, based on Reinforcement Learning (RL). The method proposed in this study is based on the study of the customer experience. To this end, a roadmap was drawn up with the stages of developing a data visualization platform. Afterward, user satisfaction was assessed using the System Usability Scale (SUS), resulting in a usability score of 81.6 points. It can be concluded that analyzing the data from this study reinforces the ongoing importance of User-Centred Product Design in creating effective and successful digital products.