Urination Sound Data Segmentation for Flow Rate Accuracy
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Solution Overview
Problem
Current methods for obtaining urination information from sound data during the urination process are inaccurate due to the inclusion of surrounding environment sounds, which complicates the analysis and leads to incorrect classification of urination sections and prediction of urine flow rates.
Innovation Solution
A method involving the use of a urination/non-urination classification model and a urine flow rate prediction model to classify urination sections and predict urine flow rates by segmenting sound data into overlapping windows, allowing for the differentiation between urination and non-urination periods and accurate prediction of urine flow rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sound data is analyzed directly without classification, then analysis process is simple, but accuracy of urination information is low due to inclusion of environment sounds
Solution Approach 1:
The patent segments the sound data into multiple windows and further divides each window into urination and non-urination sections using classification models. This segmentation allows selective analysis of only relevant sound portions, improving accuracy while managing complexity through systematic processing
Solution Approach 2:
The patent applies preliminary classification to identify urination sections before performing urine flow rate prediction. By pre-classifying sound sections and removing non-urination data, the system prepares clean input data for subsequent analysis, ensuring higher accuracy without requiring complex real-time processing
2Measurement precision
If all sound data is used for urine flow rate prediction, then data volume is sufficient, but prediction accuracy decreases due to inclusion of non-urination section data
Solution Approach 1:
The patent extracts and removes non-urination section data from the sound data using classification models. By taking out irrelevant environment sounds and keeping only urination-related sound sections, the system maintains sufficient data volume for accurate prediction while eliminating data that would degrade prediction quality
3Measurement precision
If urination classification is performed before urine flow rate prediction, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments sound data into overlapping windows and processes each window through classification and prediction models. This segmentation allows parallel processing of multiple segments, reducing overall processing time while maintaining high accuracy through systematic classification before prediction
Data Source
AI summary
A method of obtaining high accuracy urination information is proposed. There may be provided the method of obtaining the urination information, wherein sound data is divided into a plurality of windows, segmented target data corresponding to respective windows is obtained from the sound data, segmented classification data classifying urination sections or non-urination sections and segmented urine flow rate data are obtained by using the obtained segmented target data, and urination data is obtained by using the obtained segmented classification data and the segmented urine flow rate data.