Detecting microsleep in real time is crucial to facilitating the transition from semi-autonomous systems to completely autonomous driving technologies. Integrating sophisticated detection algorithms with vehicle control systems enables the provision of prompt corrective measures, such as driver alerts or temporary vehicle control. Minimizing the probability of accidents caused by driver fatigue not only improves the safety of users, but also promotes the overall security of the road network. By integrating Kalman filtering, Voronoi tessellation, and adaptive coverage control algorithms, the study seeks to find a feasible and sophisticated methodology to enhance the spatial and temporal resolution of EEG data analysis, leading to more robust and reliable detection of microsleep episodes. The paper presents a framework as a significant advancement in sleep technology, offering a new method to diagnose and understand microsleeps, characteristics, and patterns of brain activity and sleep disorders.

Voice-based disease detection with Artificial Intelligence has the potential to revolutionize healthcare, offering cost-effective, non-invasive, and accessible diagnostic methods for a wide range of diseases. The development of voice-based disease detection systems requires collaboration between multiple fields, including data science, linguistics, machine learning, and medical research. This interdisciplinary approach has led to the creation of innovative solutions that advance both healthcare and technology. By incorporating individual patient data, AI-driven voice diagnostics can provide personalized insights into a person's health, enabling tailored treatment plans that better address individual needs. The goal of the study was to find a feasible, machine learning-supported pipeline, by combining the feature extraction methods to support professional sports staff to predict disease from voice sample and prevent cross contamination in joint events.

sRPE and ACWR are valuable tools for controlling fatigue levels and minimizing injuries in performance sports. Their ability to provide individualized assessment, integrate subjective and objective measures, and inform data-driven decision-making makes them essential components of a comprehensive sports safety and performance monitoring system. The goal of this study was to provide first a computer-assisted solution to predict the fatigue level, and then to expand this with an artificial intelligence-supported solution for a more advanced pipeline in performance sports, to minimise the injury level.