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- 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.