RT info:eu-repo/semantics/article
T1 Autonomous collection of voiding events for sound uroflowmetries with machine learning
A1 Arjona, Laura
A1 Hernández, Sergio
A1 Narayanswamy, Girish
A1 Bahillo Martínez, Alfonso
A1 Patel, Shwetak
K1 Acoustics
K1 Sound sensing
K1 IoT
K1 Sound-based uroflowmetry
K1 Edge computing
K1 Machine learning
AB 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.
PB Elsevier Ltd.
SN 1746-8094
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/80193
UL https://uvadoc.uva.es/handle/10324/80193
LA eng
NO Biomedical Signal Processing and Control, 2025, vol. 105, p. 107556
NO Producción Científica
DS UVaDOC
RD 01-dic-2025