Urinary incontinence affects many women, yet there are no monitoring devices capable of accurately capturing flow dynamics during everyday activities. Building on our initial development of a wearable personal uroflowmeter, this study enhances the device's performance under realistic, dynamic conditions similar to those encountered in daily living. We integrated an optimized 8-vane Etoile flow conditioner with a 0.2D opening into the device. Both computational fluid dynamics simulations and experimental tests demonstrated that this flow conditioner significantly reduced turbulence intensity by 82% and stabilized the axial velocity profile by 67%, increasing the R² of flow rate measurements from 0.44 to 0.92. Furthermore, our machine learning frame-work—utilizing Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost) models with Principal Component Analysis (PCA)—accurately predicted the true flow rate with high correlations and robust performance with minimal overfitting. For the test dataset, the SVM achieved a correlation of 0.86, an R² of 0.74, and an MAE of 2.8, whereas the XGBoost-PCA model exhibited slightly stronger performance, with a correlation of 0.88, an R² of 0.76, and an MAE of 2.6. These advances established a solid foundation for developing a reliable, wearable uroflowmeter capable of effectively monitoring urinary incontinence in real-world settings. and 2025-05-05 Update: This version of the dataset has been refined based on reviewer comments for the article of the same name. Compared to the version uploaded on February 20, 2025, it now includes additional simulation data and the corresponding plotting code added to the "COMSOL" and "Laminar Flow" folders.
