Carlos Cunha
Carlos Augusto da Silva Cunha
0000-0002-2754-5401 • D71F-FC65-1F07
INSTITUTO POLITÉCNICO DE VISEU • Escola Superior de Tecnologia e Gestão de Viseu
Automatic Camera Calibration Using a Single Image to extract Intrinsic and Extrinsic Parameters
Carlos A. Cunha, José C. Cardoso, R. P. D. . (2024).
Automatic Camera Calibration Using a Single Image to extract Intrinsic and Extrinsic Parameters.
International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1766–1778.
Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5586
Pattern Recognition in Older Adults’ Activities of Daily Living
Augusto, G., Duarte, R., Cunha, C., Matos, A. (2024).
Pattern Recognition in Older Adults’ Activities of Daily Living.
Future Internet, 16(12), 476.
https://doi.org/10.3390/fi16120476
Automated Reusable Tests for Mitigating Secure Pattern Interpretation Errors
Cunha, C., Pombo, N. (2023).
Automated Reusable Tests for Mitigating Secure Pattern Interpretation Errors.
IEEE Access, 11: 52938-52948.
DOI: 10.1109/ACCESS.2023.3279823
Enhancing quality of life: Human-centered design of mobile and smartwatch applications for assisted ambient living
Augusto, G., Duarte, R., & Cunha, C. (2023).
Enhancing quality of life: Human-centered design of mobile and smartwatch applications for assisted ambient living.
Journal of Autonomous Intelligence, 7(1).
DOI: 10.32629/jai.v7i1.762
Agile-based Requirements Engineering for Machine Learning: A Case Study on Personalized Nutrition
Cunha, C., Oliveira, R., & Duarte, R. (2023).
Agile-based Requirements Engineering for Machine Learning: A Case Study on Personalized Nutrition.
International Journal of Intelligent Systems and Applications in Engineering, 12(2), 319–327.
Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4255
Nutrition Control System Based on Short-term Personal Demands
Cunha, C., Duarte, P., Oliveira, R. (2023).
Nutrition Control System Based on Short-term Personal Demands.
Procedia Computer Science, 224(2023), 565-571.
DOI: 10.1016/j.procs.2023.09.082
Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based Research
Duarte, R. P., Cunha, C. A. S., Alves, V. N. N. (2023).
Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based Research.
Future Internet, 15(5), 168.
http://dx.doi.org/10.3390/fi15050168
PIDI/CISeD/2022/009 • Autonomous Food Plan Adaption
Principal Researcher:
Carlos Augusto da Silva Cunha
Duration: 2022 – 2024
Cised team members
Rui Pedro Amaro Duarte
The alignment of nutrition requirements with food plan creation, follow-up, and adjustment, demands the regular gathering of biometrics, food intake habits, physical activity, and energy consumption data. Analyzing these data in the context of individual objective accomplishment provides the feedback for dynamic adjustment of food plans required to build a nutrition control system. The introduction of sensors for data-gathering activities coupled with artificial intelligence algorithms for creating personalized models has transformed nutrition into an autonomous process that can be realized without or with the minimum intervention of the nutritionist. This process is based on a person’s data with a broader spectrum than those proportioned by the follow-up of traditional nutrition. For that reason, it is potentially also more effective. Also, data availability enables food plan adjustment in shorter cycles, helping reduce the time required to meet individual objectives. This project aims to develop a nutrition control system based on data gathered by sensors (e.g., smartwatches and smart scales) for dynamic food plan adjustment, using machine learning and deep learning algorithms.
PIDI/CISeD/2022/007 • Modelos de Machine Learning para Deteção de Padrões e Preferências Alimentares
Principal Researcher: Rui Pedro Duarte
Duration: 2022 – 2024
Cised team members
Carlos Augusto da Silva Cunha
Ricardo Luís da Costa Gama
Food assumes an increasingly important role in people's lives, and adequate nutrition associated with a healthy lifestyle increases the average life expectancy. To this end, there has been an increase in the number of people whom nutritionists are following to have a food plan suited to their needs, which vary according to each person's goals: from the purely aesthetic component, through the improvement of the quality of life, for professional reasons (such as sportsmen or high competition athletes), even people with special needs, in which a correct diet impacts on the aggravation of previously diagnosed diseases. There are, however, some associated problems that can impact noncompliance with a previously defined meal plan. One of them is defining a food plan made up of foods people don't like. The other relates to real-time notification of the nutritionist of the correct fulfilment of the plan in terms of the proper intake of recommended macronutrients in each meal of the food plan.
Regarding the first, the combination of foods is a factor mainly linked to people's preferences, far beyond the rules of food combinations recommended by nutritionists. Thus, patterns for each individual may vary over time and as a function of other conditions (e.g., temperature, season). People's sensitivity to these combinations is one of the factors responsible for abandoning eating plans and not matching their food tastes. With this work, we intend to develop an Artificial Intelligence model to detect food patterns to adapt a food plan defined by a nutritionist in an evolutionary way and in real-time to allow the correct management of the plan. Thus, it becomes possible to provide a better quality of life to people who need to define food plans in various types of contexts.