Percorrer por autor "Morgado-Dias, Fernando"
A mostrar 1 - 3 de 3
Resultados por página
Opções de ordenação
- Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: a systematic reviewPublication . Gupta, Ankit; Ravelo-García, Antonio G.; Dias, Fernando Morgado; Gupta, Ankit; Ravelo-García, Antonio G.; Morgado-Dias, FernandoBackground: Consumer-level cameras have provided an advantage of designing cost-effective, non-contact physiological parameters estimation approaches which is not possible with gold standard estimation tech niques. This encourages the development of non-contact estimation methods using camera technology. Therefore, this work aims to present a systematic review summarizing the currently existing face-based non-contact methods along with their performance. Methods: This review includes all heart rate (HR) and oxygen saturation (SpO2) studies published in journals and a few reputed conferences, which have compared the proposed estimation methods with one or more standard reference devices. The articles were collected from the following research databases: In stitute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science (WoS), Science Direct, and Association of Computer Machinery (ACM) digital library. All database searches were completed on May 20, 2021. Each study was assessed using a finite set of identified factors for reporting bias. Results: Out of 332 identified studies, 32 studies were selected for the final review. Additionally, 18 studies were included by thoroughly checking these studies. 3 out of 50 (6%) studies were performed in clinical conditions, while the remaining studies were carried out on a healthy population. 42 out of 50 (84%) studies have estimated HR, while 5/50 (10%) studies have measured SpO2 only. The remaining three stud ies have estimated both parameters. The majority of the studies have used 1–3 min videos for estimation. Among the estimation methods, Deep Learning and Independent component analysis (ICA) were used by 11/42 (26.19%) and 9/42 (21.42%) studies, respectively. According to the Bland-Altman analysis, only 8/45 (17.77%) HR studies achieved the clinically accepted error limits whereas, for SpO2, 4/5 (80%) studies have matched the industry standards (±3%). Discussion: Deep Learning and ICA have been predominantly used for HR estimations. Among deep learn ing estimation methods, convolutional neural networks have been employed till date due to their good generalization ability. Most non-contact HR estimation methods need significant improvements to im plement these methods in a clinical environment. Furthermore, these methods need to be tested on the subjects suffering from any related disease. SpO2 estimation studies are challenging and need to be tested by conducting hypoxemic events. The authors would encourage reporting the detailed information about the study population, the use of longer videos, and appropriate performance metrics and testing under abnormal HR and SpO2 ranges for future estimation studies.
- Intelligent visibility forecasting at airports: a systematic reviewPublication . Alves, Décio; Belo-Pereira, Margarida; Mendonça, Fábio; Morgado-Dias, Fernando; Alves, Decio; Silva Mendonça, Fábio Rúben; Morgado-Dias, FernandoAbstract Low visibility conditions caused by phenomena such as fog, heavy rain, or snowfall impose major operational and safety challenges at airports. Conventional numerical weather prediction models, although improved over time, still struggle to forecast low visibility accurately due to scale mismatches and uncertainties in physical parameterizations, among other factors. This review synthesizes findings on data-driven solutions, including machine learning and deep learning, that harness large datasets to reveal hidden patterns, offering better performance and adaptability. Ensemble methods and the integration of multiple data sources further enhance accuracy, particularly for short lead times. Several methods achieve correlation coefficients above 0.90 and root mean square error below 1 km, yet generalization and integration into real-time airport operations remain underexplored. Future work should focus on transferability across diverse climates, integration with advanced operational tools, and bridging gaps between model complexity and user interpretability. The next generation of hybrid forecasting frameworks has the potential to enhance safety, limit economic losses, and improve resilience in airport operations.
- Machine learning system for commercial banana harvestingPublication . Hayat, Ahatsham; Baglat, Preety; Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Morgado-Dias, Fernando; Baglat, Preety; Silva Mendonça, Fábio Rúben; Morgado-Dias, FernandoAbstract The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learning could significantly impact the visual inspection process domains, allowing consistent harvesting and grading. To achieve the goal of the industry-level harvesting process, this work collects data from professional harvesters from the industry. It investigates six state-of-the-art architectures to find the best solution. 2,685 samples were collected from four different sites with expert opinions from industry harvesters to cut (or harvest) and keep (or not harvest) the banana brunch. Comparative results showed that the DenseNet121 architecture outperformed the other examined architectures, reaching a precision, recall, F1 score, accuracy, and specificity of 85%, 82%, 82%, 83%, and 83%, respectively. In addition, an understanding of the underlying black box nature of the solution was visualized and found adequate. This visual interpretation of the model supports human expert’s criteria for harvesting. This system can assist or replace human experts in the field.