Various factors, such as hydration levels, urinary tract diseases, prostatic hyperplasia, neurological disorders, medications, diabetes, and renal failure, can affect urination. This article explores the possibility of continuously evaluating urinary health using IoT technology by employing a contact microphone attached to the outside of the toilet bowl to record the acoustic patterns of urination for subsequent analysis. The performance of several algorithms for detecting urination patterns was investigated. Acoustic recordings were divided into segments of different sizes, from which 11 features were extracted. Support Vector Machines (SVM) were then used to assess the algorithm's effectiveness with various combinations of features and segment sizes. The aim of this study is to investigate the effectiveness of different methods for detecting acoustic patterns of urination, providing a range of algorithmic alternatives adaptable to the available processing capacity for detection.

User interfaces (UIs) play a crucial role in defining user experiences and influencing the success of software products. While UI design has traditionally been subjective and iterative, data-driven approaches are becoming increasingly popular to ensure that Uis meet user needs and expectations. However, contextual factors such as the application domain can present challenges for designing Uis that are both effective and efficient. This is particularly true in the health domain, where Uis must be adapted to specific tasks and user expertise to maximize the support provided by software systems. Moreover, the urgency of delivering fully functional systems in short periods can relegate UI design to a second plane. This paper presents a framework proposal for refactoring and improving Uis using a data-driven approach, providing an efficient and systematic methodology to address not solved UI issues introduced during previous software development processes. The proposed framework has been successfully applied to two medical platforms, demonstrating the importance of data-driven approaches for UI refactoring in domains with particular necessities.