Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

Machine learning algorithms have been proven to be effective in supporting medical decisions. However, often the resulting models are difficult to understand by clinicians. Fuzzy Inference Systems are effective in returning interpretable models that can be easily described by IF-THEN rules expressed in natural language. Yet, conventional fuzzy systems require human expertise to design the fuzzy rule base. Neuro-fuzzy systems are data-driven algorithms, that are able to learn fuzzy rule-based models from data, thus avoiding the time consuming task of manually designing the rules. In this work we propose the use of neuro-fuzzy systems to learn predictive models from data in form of fuzzy rules that are suitable to support decision making for cardiovascular risk assessment. Our results show the effectiveness of these models in returning accurate predictions while preserving the interpretability. Furthermore, our results indicate that the models derived automatically are more accurate than the models defined manually, with the best accuracy of 0.91.

This paper introduces FISDeT, a tool to support the design of Fuzzy Inference Systems, composed of a set of Python modules sharing the standard specification language FCL used for FIS definition. FISDeT includes a graphical user interface that enables easy definition and quick update of elements composing theknowledgebaseofaFIS.Giventheknowledgebase,thetoolcanperformtheinferenceoffuzzyrules, providingtheoutputofaFISforanygiveninput. Modulesforcreatingafuzzyrulebaseforclassification and verifying the behavior of the classification system are integrated within the environment. The paper also reports applications of FISDeT to acquire available knowledge bases and create a FIS for an image processing task

This study explores the use of explainable artificial intelligence in education, with a focus on its relevance for Learning Analytics. The research introduces a prototype-based dynamic incremental classification algorithm, Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM), which leverages fuzzy logic to analyze student interaction data from virtual learning platforms, even when the data are only partially labeled. The proposed methodology generates human-centered explanations by extracting IF-THEN fuzzy rules from the evolving prototypes produced by DISSFCM over successive time intervals. These explanations, expressed in linguistic terms, remain accessible to non-expert stakeholders and are particularly suitable for educational contexts. The Open University Learning Analytics Dataset (OULAD) is utilized for experimentation and validation, providing a realistic scenario for semi-supervised data collection. Visual summaries of the evolving fuzzy rules support the identification of temporal patterns in streaming data. Results show that the model effectively adapts to concept drift while maintaining interpretability. Most notably, it proves robust in handling partially labeled data and variable time granularities, two challenges frequently encountered in real-world Learning Analytics scenarios. The ability to both predict student outcomes and provide intelligible explanations under such constraints highlights the practical value of the approach. To evaluate the quality and relevance of the generated explanations, an expert-based evaluation was conducted. Domain experts evaluated the clarity, usefulness, and accuracy of the explanations in terms of their support for human understanding and decision-making. The results suggest that the explanations were perceived as generally informative and useful, supporting the method’s relevance for human-centered educational applications.