Jeffrey Andrew Tuhtan

This project is based on the new paradigm of "flow as information", a groundbreaking approach for underwater sensing of multiscale flows in Nature. It will lead to new, optimized devices and methods to measure, classify and explore the underwater environment when traditional methods are too expensive or simply do not work. Flow as information is inspired by aquatic animals who have evolved advanced sensory systems which combine sensing and information processing into a single framework. The proposal will advance TalTech's underwater sensing technologies from working prototypes (TRL3 to TRL5) to tests in relevant operational environments (TRL6), and support technology transfer to Estonian and international firms. These devices and methods will provide researchers, industry and authorities with new and reliable sources of flow data during extreme climate and weather events where conventional devices fail and when critical infrastructure is at risk, such as during storm surges and floods.

Data has become the most valuable resource for the automation and optimization of tasks arising both in the private and public spheres. The proposed research area/project aims at strengthening both the synergy and quality of the current research of Taltech in this area, while significantly enhancing the capabilities of Uni to cooperate with Estonian industry and public sphere by joint work, consultations, continuous and regular education. The focus of the project is on using machine learning for data science: ML, in particular deep learning, has shown the most promise in advancing the capabilities of future software systems and empowering the whole business of software development. The concrete goal is to increase the manpower and competence in machine learning, while enhancing and cooperating with the existing areas of data science like data and rule mining, data semantics and knowledge representation, natural language data queries, data integration, statistics and data management.

Falls and frailty are major concerns for the aging population, with ~30% of people over the age of 65 falling each year. Current risk assessments mainly rely on self-reports and subjective scales, failing to account for the interplay between mobility, behavior, and environment. As a result, early signs of declining health often go unnoticed. The SAFE project aims to develop a sensor-based framework for real-world fall-risk profiling. SAFE focuses on balance losses that do not lead to actual falls, which are underestimated risk factors, using wearable sensors. The project (1) identifies risk factors and sensor-based metrics of instability, (2) validates the technology's performance with older adults living in the community, and (3) investigates the impact of behavior and context on fall risk. SAFE moves from detecting consequences to a fall-prevention system, enabling a personalized approach to fall-risk assessment and supporting healthier aging among the European population.