Development of an Ultrasonic Platform for Privacy-Preserving Autonomous Sound Uroflowmetry at Home

This thesis focuses on developing an ultrasonic platform for privacy-preserving autonomous sound uroflowmetry at home. The research work is structured into three main stages, including generating a sound database, dataset preparation, and developing artificial intelligence (AI) models for flow rate extraction. My involvement in these tasks has led to acquiring knowledge in audio signal processing, flow rate analysis, water acoustics, and applying machine learning models to audio signals. This research aims to address the challenge of enabling individuals to conduct urinary flow measurements autonomously within the privacy of their homes.