Autonomous collection of voiding events for sound uroflowmetries with machine learning
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Fecha
2025-02-04
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Elsevier Ltd
Resumen
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.
Palabras clave
Acoustics
Edge computing
IoT
Machine learning
Sound sensing
Sound-based uroflowmetry
Edge computing
IoT
Machine learning
Sound sensing
Sound-based uroflowmetry
Descripción
Materias
Cita
Arjona, 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.2025.107556