Arjona Aguilera, LauraHernández López, SergioNarayanswamy, GirishBahillo, AlfonsoPatel, Shwetak2026-01-092026-01-092025-02-04Arjona, L., Hernández, S., Narayanswamy, G., Bahillo, A., & Patel, S. (2025). Autonomous collection of voiding events for sound uroflowmetries with machine learning. Biomedical Signal Processing and Control, 105. https://doi.org/10.1016/J.BSPC.2025.1075561746-809410.1016/J.BSPC.2025.107556https://hdl.handle.net/20.500.14454/4652We present AutoFlow, a Raspberry Pi-based acoustic platform that uses machine learning to autonomously detect and record voiding events. Uroflowmetry, a noninvasive diagnostic test for urinary tract function. Current uroflowmetry tests are not suitable for continuous health monitoring in a nonclinical environment because they are often distressing, costly, and burdensome for the public. To address these limitations, we developed a low-cost platform easily integrated into daily home routines. Using an acoustic dataset of home bathroom sounds, we trained and evaluated five machine learning models. The Gradient Boost model on a Raspberry Pi Zero 2 W achieved 95.63% accuracy and 0.15-second inference time. AutoFlow aims to enhance personalized healthcare at home and in areas with limited specialist access.eng© 2025 The AuthorsAcousticsEdge computingIoTMachine learningSound sensingSound-based uroflowmetryjournal article2026-01-091746-8108