My research focus is Human activity recognition in indoor environments using multimodal sensing techniques. My doctoral research develops novel methods for identifying daily activity patterns through fusion of data from motion capture systems, sensor-equipped floors, wearable accelerometers, and distributed IoT sensors. This work aims to enhance ambient assisted living solutions through improved contextual awareness and behavioral pattern recognition in smart home environments.

• Management and development of integrated systems in smart home laboratories, including configuration of IoT devices and databases for data collection.
• Implementation of algorithms for analyzing user behavior in smart homes using Python (Pandas, NumPy, scikit-learn).
• Supervision and consultation for bachelor’s and master’s theses focused on data processing and software development.
• Creation of embedded systems for collection and analysis of biomedical data using C++ and microcontrollers.

My work included developing software solutions for gait analysis, implementing biomedical image processing algorithms for diagnostics, and assessing the efficiency of rehabilitation accessories. These projects involved quantitative data analysis to provide healthcare professionals with evidence-based tools for evaluating patient progress and treatment effectiveness.

• Conducting complex data analyses and statistical evaluations within clinical studies using Python and R.
• Co-author of 3 scientific publications in the field of biomedicine and data analysis published in international journals.
• Creation of visualizations and dashboards for interpreting complex biomedical data.

• Leading workshops on data analysis and programming for high school students (10+ events).
• Presentation of research results at conferences and popularization events.
• Preparation and publication of educational materials on the use of IoT and data analysis in everyday life.

During my six-month Erasmus+ internship at IBISC Laboratory (University of Evry, France), I focused on indoor positioning and location-based technologies. The role involved working with microcontroller systems and Bluetooth LE for spatial tracking applications, as well as developing and testing smartphone applications for indoor localization. This experience complemented my master’s thesis on indoor localization using Bluetooth LE and provided valuable international research exposure.