UroSound

Automatic Classification of Audio Uroflowmetry with a Smartwatch

Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. This work presents the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. Several classification models are built using classical machine learning and report a maximal test accuracy of 86.16% using an ensemble method classifier. This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous audio capture devices such as smartphones and smartwatches.

Publications

[1] Girish Narayanswamy, Laura Arjona, Luis E. Diez, Alfonso Bahillo, Shwetak Patel.
To appear in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022

Additional Material

EMBC Poster

Collaborators

Laura Arjona, Luis E. Diez Alfonso Bahillo, Shwetak Patel,