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Uroflowmetry (UF) is a common clinic-based non-invasive test to diagnose Lower Urinary Tract Dysfunction (LUTD). Accurate home-based uroflowmetry methods are needed to conveniently conduct repeated uroflowmetries when patients are physiologically ready to urinate. To this end, we propose and evaluate a novel mobile sonouroflowmetry (SUF) method that estimates the urinary flow rate from a sound signal recorded using a mobile phone. By linearly mapping the total sound energy to the total voided volume, the sound energy curve is transformed to a flow rate curve allowing the estimation of the flow rate over time. An evaluation using data from 44 healthy young men showed high similarity between the UF and SUF flow rates with a mixed-effects model correlation coefficient of 0.993 and a mean root mean square error of 2.37 ml/s. Maximum flow rates were estimated with an average absolute error of 2.41 ml/s. Future work on mobile uroflowmetry can use these results as an initial benchmark for flow rate estimation accuracy.

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A fundamental task for a robotic audition system is sound source localization. This paper addresses the localization problem in a robotic humanoid context, providing a novel learning algorithm that uses binaural cues to determine the sound source's position. Sound signals are extracted from a humanoid robot's ears. Binaural cues are then computed to provide inputs for a neural network. The neural network uses pixel coordinates of a sound source in a camera image as outputs. This learning approach provides good localization performances as it reaches very small errors for azimuth and elevation angles estimates.

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