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Annotated dataset of simulated voiding sound for urine flow estimation.
Marcos Lazaro Alvarez1, Laura Arjona2, Alfonso Bahillo3
1Faculty of Engineering, University of Deusto, Bilbao, 48007, Spain. alvarez.marcoslazaro@deusto.es.
Scientific Data
|June 13, 2025
Summary
This study introduces a new dataset of simulated urinary flow sounds to advance artificial intelligence in sound-based uroflowmetry. This resource aids in developing AI algorithms for non-invasive urinary flow estimation.
Area of Science:
- Biomedical Engineering
- Acoustics
- Artificial Intelligence
Background:
- Sound-based uroflowmetry offers a non-invasive method for assessing urinary voiding characteristics.
- Current research is hindered by a lack of labeled datasets for developing supervised AI algorithms.
Purpose of the Study:
- To create a comprehensive dataset of simulated urinary flow sounds.
- To support the development and validation of AI algorithms for sound-based uroflowmetry.
Main Methods:
- Generated simulated urinary flow sounds using a peristaltic pump and a nozzle simulating the urethra.
- Recorded sounds at flow rates from 1 to 50 ml/s in a real toilet bowl.
- Acquired simultaneous audio recordings using a high-quality microphone, smartphone, and smartwatch.
Main Results:
- A dataset of labeled, 60-second constant-flow audio recordings in WAV format was created.
- Recordings cover flow rates from 1 to 50 ml/s, mimicking adult urination conditions.
- The dataset includes recordings from multiple devices, offering diverse data.
Conclusions:
- The presented dataset is a valuable resource for advancing sound-based uroflowmetry research.
- Facilitates the development of supervised AI models for accurate urinary flow estimation.
- Promotes innovation in non-invasive diagnostic tools for urological conditions.
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