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- 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.
- 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.
- 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.
- Professional Development for Higher Education Teaching Staff: An Experience of Peer Learning in a Portuguese PolytechnicPublication . Figueiredo, Maria Pacheco; Matias, Rogério; Alves, Valter; Bastos, Nuno; P. Duarte, Rui; Ferreira, Bruno; Cunha, CarlosSeveral challenges have been tackled by Portuguese (Fernandes, 2016; Mendes et al., 2016) and European Higher Education, many of which are in the pedagogical arena. Although Pedagogy in Higher Education is not an invested area, there are studies, initiatives, projects, and structures of great quality in the national context (Costa, 2019; Fernandes, 2016; Gonçalves et al., 2010; Vieira et al., 2016; Pêgo & Mouraz, 2017; Vieira, 2017). An important part of the advances in the area has resulted from the systematic analysis, sharing, and discussion of practices that have embodied several publications and sustained several interventions in Portugal and internationally. The EQuIPES - Study of Quality and Pedagogical Innovation in Higher Education aims to contribute to this body of experiences and studies that allows to understand and improve teaching and learning in Higher Education institutions, based on the analysis of practices in the Polytechnic of Viseu (PV) in communication with external partners. The PV has five schools with a teaching staff of 400 members for 5400 students. The programs range from professional learning (level 5, no degree) to Masters level, with a large percentage of students enrolled in Bachelor degrees from different areas (teacher education, social work, and education, nursing, community health, engineering, arts, design and multimedia, marketing, management, tourism, media studies, agriculture, public relations, information and communication technologies, and sports). The majority of PV’s study programs are professionally oriented, in line with the mission of the polytechnic Higher Education system. This holds true for EQF level 5 programs but also for the BAs and MAs. Each program is designed in close proximity and alignment with professional contexts and practices. The supervised practical training in professional contexts is combined with project-based learning throughout the programs and there is a strong emphasis on active learning strategies. These pose challenges for Pedagogy and pedagogical knowledge and competencies, developed in the workplace through action, but also through reflection (Kuh et al., 2010). Peer learning and discussion groups are important for supporting those processes. Pedagogical action becomes something that is shared, regardless of scientific areas (Behrens & Junges, 2018). In the discussion of pedagogical practices, a focus on the learning and the students is important and technology has been highlighted as facilitating that shift (Sharples, 2016). In the past year, a group of seven professors developed a set of opportunities to share and discuss practices, named “Apps & Things”, that ran 10 workshops. In this paper, we will present the set of workshops and analyze the pedagogical elements of each one, as well as how the technology was articulated with the pedagogical elements. The pedagogical elements identified were: assessment and evaluation; communication and interaction; planning and monitoring work; distance learning; students’ participation; and trust. The results also include how the colleagues that participated in the workshops (around 25 in each) valued the pedagogical elements and what challenges were shared regarding them The experience is discussed in terms of the EQuIPES framework, intending to contribute to the visibility of Pedagogy in Higher Education, associated with peer-to-peer training opportunities and experiences.
- Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based ResearchPublication . P. Duarte, Rui; Cunha, Carlos; Alves, ValterAlzheimer’s disease is a type of dementia that affects many individuals, mainly in an older age group. Over time, it leads to other diseases that affect their autonomy and independence. The quality of food ingestion is a way to mitigate the disease and preserve the patient’s well-being, which substantially impacts their health. Many existing applications for food plan management focus on the prescription of food plans but do not provide feedback to the nutritionist on the real amount of ingested calories. It makes these applications inadequate for these diseases, where monitoring and control are most important. This paper proposed the design and development of a mobile application to monitor and control the food plans of Alzheimer’s patients, focused on informal caregivers and respective patients. It allows both the realistic visualization of the food plans and users to adjust their consumption and register extra meals and water consumption. The interface design process comprises a two-level approach: the user centered design methodology that accounts for users’ needs and requirements and the user experience questionnaire to measure user satisfaction. The results show that the interface is intuitive, visually appealing, and easy to use, adjusted for users that require a particular level of understanding regarding specific subjects.