2026-04-27T21:30:27Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/801932025-12-01T20:09:01Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Arjona, Laura Hernández, Sergio Narayanswamy, Girish Bahillo Martínez, Alfonso Patel, Shwetak Producción Científica We 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. 2025-12-01 2025-12-01 2025 info:eu-repo/semantics/article Biomedical Signal Processing and Control, 2025, vol. 105, p. 107556 1746-8094 https://uvadoc.uva.es/handle/10324/80193 10.1016/j.bspc.2025.107556 107556 Biomedical Signal Processing and Control 105 eng https://www.sciencedirect.com/science/article/pii/S1746809425000679 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución 4.0 Internacional Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier Ltd.