Loading...
15 results
Search Results
Now showing 1 - 10 of 15
- Nutrition Control System Based on Short-term Personal DemandsPublication . Cunha, Carlos; P. Duarte, Rui; Oliveira, RafaelPersonalized nutrition considers an individual’s unique genetic, metabolic, and lifestyle factors to create a customized dietary plan tailored to their needs. People seeking to optimize their health and wellness through diet and lifestyle changes can benefit from technological advances in machine learning and deep learning approaches to create personalized models of nutritional requirements that override traditional food plans. These models will provide users with an unprecedented decision tool for informing them of the impact of specific food intake and exercise on their goals. This article presents the architecture, implementation, and preliminary results of a deep learning-based control system for nutrition. It allows users to understand the impact of their food and exercise immediate choices on their goals while reducing user interaction demands. Preliminary results have shown that it is possible to predict BMI (Body Mass Index) accurately within a time window of three days.
- Automated Reusable Tests for Mitigating Secure Pattern Interpretation ErrorsPublication . Cunha, Carlos; Pombo, NunoThe importance of software security has increased along with the number and severity of incidents in recent years. Security is a multidisciplinary aspect of the software development lifecycle, operation, and user utilization. Being a complex and specialized area of software engineering, it is often sidestepped in software development methodologies and processes. We address software security at the design level by adopting design patterns that encapsulate reusable solutions for recurring security problems. Design patterns can help development teams implement the best-proven solutions for a specialized problem domain. However, from the analysis of three secure pattern implementations by 70 junior programmers, we detected several structural errors resulting from their interpretation. We propose reusable unit testing test cases based on annotations to avoid secure pattern interpretation errors and provide an example for one popular secure pattern. Providing these test cases to the same group of programmers, they implemented the pattern without errors. The reason is annotations build a framework that disciplines programmers to incorporate secure patterns in their applications and ensure automatic testing.
- Automatic Camera Calibration Using a Single Image to extract Intrinsic and Extrinsic ParametersPublication . P. Duarte, Rui; Cunha, Carlos; Pereira Cardoso, José CarlosThis article presents a methodology for accurately locating vanishing points in undistorted images, enabling the determination of a camera's intrinsic and extrinsic parameters as well as facilitating measurements within the image. Additionally, the development of a vanishing point filtering algorithm is introduced. The algorithm's effectiveness is validated by extracting real-world coordinates using only three points and their corresponding distances. Finally, the obtained vanishing points are compared with extrinsic parameters derived from multiple objects and with intrinsic parameters obtained from various shapes and images sourced from different test sites. Results show that through a single image, the intrinsic parameters are extracted accurately. Moreover, Using 3 points to determine the extrinsic parameters is an excellent alternative to the checkerboard, making the method more practical since it does not imply the manual positioning of the checkerboard to perform the camera calibration.
- Predictive Model for Estimating Body Weight Based on Artificial Intelligence: An Integrated Approach to Pre-processing and EvaluationPublication . Figueiredo, Diana; Duarte, A. P.; Cunha, CarlosBody weight is much more than just a number on a scale. This value can indicate various diseases, as both excess and insufficient weight have implications for an individual’s health. Excess weight is associated with heart disease, obesity, diabetes, high blood pressure, and respiratory disorders, among others. Meanwhile, extreme underweight is associated with problems such as nutritional deficiency, weakened immune system, osteoporosis, and hormonal imbalances. Due to these issues, there is a need to monitor and analyse body changes to adopt a diet and lifestyle balanced with individual needs. The weight control process is complicated and depends on various factors. This paper aims to develop a machine-learning model to predict future weight based on dietary records, physical exercise, and basal metabolic rate to demonstrate three days’ impact on future weight. Results of the model’s performance show that the coefficient of determination yielded a value of 0.75, which is considered good for this metric. The mean square and absolute errors demonstrate that the model could learn patterns in the data without significant overfitting.
- Are you lost? Using Facial Recognition to Detect Customer Emotions in Retail StoresPublication . Borges, Valter; P. Duarte, Rui; Cunha, Carlos; Mota, David; ThinkmindThe understanding of consumer behavior is a dynamic field, especially relevant to the success of companies and for consumer satisfaction. It is especially important in the situation of intense competition, currently characteristic for the retail store industry, where companies fight for every individual customer. Moreover, companies do not want customers to enter their system and leave without buying products they intended to buy. This has an impact on user satisfaction and retail stores income. In this paper we present a method that targets customer satisfaction in the aforementioned context using a facial recognition system acting at the emotional level of the customer. Our method is based on cumulative negative emotions that are associated to a sadness level, which triggers events for retail store assistants to help customers. Results show that this method is adequate to measure these emotions and is a useful reference for retail store assistant intervention.
- Automatic User Testing and Emotion Detection in Interactive Urban DevicesPublication . P. Duarte, Rui; Cunha, Carlos; Pereira Cardoso, José CarlosAutomated testing and evaluation of interfaces is a well-established reality supported by many tools that shorten the time to deploy new software versions to the user. However, exploring users’ emotions while interacting with interfaces as a tool to further increase the quality of traditional usability evaluation methods is still far from being a reality. This work uses the automatic analysis of users’ emotions while interacting with touchable interactive urban devices to detect usability issues. To this end, a coupled approach is implemented: the data is acquired from the interaction, and user emotions are extracted and processed to determine the emotional status during the interaction. This data is integrated into a web application so that designers can further improve the quality of the interface in the presence of negative emotions. Results show that the experimental tests showed that different users manifest similar negative emotions in the same contexts, which is a clear sign of usability issues that are to be corrected by the design team.
- Meal Suggestions for Caregivers and Indecisive Individuals Without a Set Food PlanPublication . Cunha, Carlos; Cardoso, Tiago R.; P. Duarte, RuiRecommendation systems have played a crucial role in assisting users with decision-making across various domains. In nutrition, these systems can provide valuable assistance by offering alternatives to inflexible food plans that often result in abandonment due to personal food preferences or the temporary unavailability of certain ingredients. Moreover, they can aid caregivers in selecting the most suitable food options for dependent individuals based on their specific daily goals. In this article, we develop a data-driven model using a multilayer perceptron (MLP) network to assist individuals in making informed meal choices that align with their preferences and daily goals. Our study focuses on predicting complete meals rather than solely on predicting individual food items since food choices are often influenced by specific combinations of ingredients that work harmoniously together. Based on our evaluation of a comprehensive dataset, the results of our study demonstrate that the model achieves a prediction accuracy of over 60% for an individual complete meal.
- Unveiling Neural Networks for Personalized Diet RecommendationsPublication . Cunha, Carlos; Rebelo, João; P. Duarte, RuiThe growing prevalence of poor nutrition is a major public health concern, as it fuels the rise of various diseases. Obesity, a silent and rapidly growing threat linked to unhealthy eating, is a prime example. Despite the abundance of information on diets and recipes, finding a personalized approach to healthy eating can be a challenge. Recommendation systems can filter from a food logging dataset the information that best suits the nutrition profile of a given user. A powerful tool to use in food recommendation systems is neural networks. However, the user’s available data are often limited, which compromises the performance of neural-based food recommendation models. To enhance user trust in food recommendations, this paper proposes a method using a secondary model to predict the errors of the primary neural network, especially when dealing with limited data.
- Pattern Recognition in Older Adults’ Activities of Daily LivingPublication . Augusto, Gonçalo F.; P. Duarte, Rui; Cunha, Carlos; Matos, AnaMonitoring daily activities and behaviors is essential for improving quality of life in elderly care, where early detection of behavioral anomalies can lead to timely interventions and enhanced well-being. However, monitoring systems often struggle with scalability, high rates of false positives and negatives, and lack of interpretability in understanding anomalies within collected data. Addressing these limitations requires an adaptable, accurate solution to detect patterns and reliably identify outliers in elderly behavior data. This work aims to design a scalable monitoring system that identifies patterns and anomalies in elderly activity data while prioritizing interpretability to make well-informed decisions. The proposed system employs pattern recognition to detect and analyze outliers in behavior analysis, incorporating a service worker generated with Crontab Guru for automated data gathering and organization. Validation is conducted through statistical measures such as accumulated values, percentiles, and probability analyses to minimize false detections and ensure reliable performance. Experimental results indicate the system achieves high accuracy, with an occupancy probability across compartments and fewer outliers detected. The system demonstrates effective scalability and robust anomaly detection. By combining pattern recognition with a focus on interpretability, the proposed system provides actionable insights into elderly activity patterns and behaviors. This approach enhances the well-being of older adults, offering caregivers reliable information to support timely interventions and improve overall quality of life.
- Enhancing quality of life: Human-centered design of mobile and smartwatch applications for assisted ambient livingPublication . Augusto, Gonçalo F.; P. Duarte, Rui; Cunha, CarlosBackground: Assisted ambient living interfaces are technologies designed to improve the quality of life for people who require assistance with daily activities. They are crucial for individuals to maintain their independence for as long as possible. To this end, these interfaces have to be user-friendly, intuitive, and accessible, even for those who are not techsavvy. Research in recent years indicates that people find it uncomfortable to wear invasive or large intrusive devices to monitor health status, and poor user interface design implies a lack of user engagement. Methods: This paper presents the design and implementation of non-intrusive mobile and smartwatch applications for detecting older adults when executing their routines. The solution uses an intuitive mobile application to set up beacons and incorporates biometric data acquired from the smartwatch to measure bio-signals correlated to the user’s location. User testing and interface evaluation are carried out using the User Experience Questionnaire (UEQ). Results: Six older adults participated in the evaluation of the interfaces. Results show that users found the interaction to be excellent in all the parameters of the UEQ in the evaluation of the mobile interface. For the smartwatch application, results vary from above average to excellent. Conclusions: The applications are intuitive and easy to use, and data obtained from integrating systems is essential to link information and provide feedback to the user.