The purpose of this study was to explore the feasibility of a urination management system by developing a smart band-based algorithm that recognizes the urination interval of women. We designed a device that recognizes the time and interval of urination based on the patient’s specific posture and posture changes. The technology used for recognition applied the Radial Basis Function kernel-based Support Vector Machine, a teaching and learning method that facilitates multidimensional analysis by simultaneously judging the characteristics of complex learning data. In order to evaluate the performance of the proposed recognition technique, we compared actual urination and device-sensed urination. An experiment was performed to evaluate the performance of the recognition technology proposed in this study. The efficacy of smart band monitoring urination was evaluated in 10 female patients without urination problems. The entire experiment was performed over a total of 3 days. The average age of the participants was 28.73 years (26–34 years), and there were no signs of dysuria. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 91.0%, proving the robustness of the proposed algorithm. This urination behavior recognition technique shows high accuracy and can be applied in clinical settings to characterize urination patterns in female patients. As wearable devices develop and become more common, algorithms that detect specific sequential body movement patterns that reflect specific physiological behaviors could become a new methodology to study human physiological behavior.
A variety of tools are used to manage patients with urinary disorders, such as voiding chart. Textbooks also present a voiding diary as one of the most important diagnostic tools for dysuria (
The technique proposed in this study is a technique of extracting and learning the characteristics of the urination signal pattern by determining which boundary the newly input urination activity signal is included in, and calculating the final urination activity. Various methods and learning algorithms for motion recognition have been presented. Most studies have used static algorithms such as artificial neural networks and K-means, or dynamic time warping in combination with algorithms (
In this study, the characteristics of the extracted composite data were simultaneously identified and the Radial Basis Function (RBF) kernel-based Support Vector Machine (SVM) method suitable for multidimension analysis was applied. The goal is to utilize the proposed technology to improve users’ perception of urination and to efficiently solve existing problems, such as problems where existing data do not apply. The conceptual diagram used to recognize urination in female patients was processed according to the following steps as shown in
This research was approved by the Institutional Review Board of Gachon University Gil Medical Center (approval number: GDIRB2017-096). The average age was 28.73 years (26–34 years), with no sign of urination disorder. The user wrote a urination diary for 3 days and monitored the number of urinations by wearing a smart band. The smart band used in this study recognized the urination activity by combining the female patient’s gyro sensor information and the ground height information, the distance information. In this study, based on the changes in the patient’s posture and posture, the RBF kernel-based SVM was applied to increase the accuracy in urination study. In the case of an existing study (
Support vectors are data points located at the boundary between two classes. There is a lot of data, but support vectors among them influence the creation of decision boundaries. However, most data are not ideally separated. In many cases, outliers are observed. In this case, it is impossible to isolate the data linearly and completely. To solve this problem, a strategy has been created to allow some errors. The parameter associated with this is cost. Cost determines how many data samples are allowed to be placed in different classes. The smaller, the more allowed, and the larger, the less allowed. In other words, setting low-cost value will catch a lot of anomalies and find common decision boundaries, while setting a high setting will make the ideal probability smaller, making it prudent. Also, kernel technology is to think of the given data as a high-level characteristic space. Once you think in high-dimensional space, you can classify them into linear or nonlinear forms that were never seen in the original dimension. For the RBF kernel, the parameter gamma must be adjusted by the user. There is also C, the default parameter for the SVM, so a total of two parameters must be set. Gamma determines the distance at which a single sample of data exerts influence. Gamma is associated with the standard deviation of the Gaussian function, and the larger, the smaller the standard deviation. In other words, the larger the gamma, the shorter the distance a data pointer exerts influence. Through this, a method for recognizing urination activity by simultaneously judging a female patient’s gyro sensor information and ground height distance information along with characteristics was proposed. In this study, data from 10 women were studied to optimize the learning model, i.e., the optimal boundary line was trained to establish cost and gamma values. The best criterion is to determine the parameter values for calculating the optimal position of the boundary line for data classification with adequacy of the margins.
The performance of SVM-based urination recognition technology was evaluated using statistical analysis based on confusion matrix. The number of manually recorded urination and the number of actual recognized waveforms were compared to measure true positives, false positives, and false negatives. Sensitivity and specificity were calculated to verify the effectiveness and accuracy of the proposed urination recognition technology. Statistical analysis basically included correlation, standard deviation, and analysis of variance. Although the number of pretreatment was different for each female patient, the recognition criteria were the same, so the comparison of actual waveform recognition accuracy was carried out without any problems.
We evaluated the efficacy of smart band monitoring urination in 10 female patients without urination problems. The entire experimental period was carried out over a total of 3 days. The odd percentage of urination reported in smart band was 32.83 (95% confidence interval [CI], 20.52–52.52). The heterogeneity between the participants was not identified (
Many studies on the application of mobile health have achieved results in various medical fields. In the field of urology, there are many attempts to develop mobile medical devices for both patients and clinicians. As urination problems limit daily life, urination disorders are prevalent and associated with reduced quality of life due to a variety of urinary disorders (
In this paper, an algorithm development study for recognition of urinary activity of female urinary patients was conducted by applying RBF kernel-based SVM, and the feasibility of urination management system was verified through performance evaluation.
Interim analysis of smart medical devices for our final project showed that human behavior objects using wearable devices were highly reliable and efficient. The results were found to be positive and sensitive. In the future, we plan to utilize the Dempster-Shafer theory to increase the recognition accuracy of this algorithm. Additionally, electronic and mobile urination journals have shown several benefits. Previous studies to evaluate mobile medical applications or devices were conducted with people familiar with mobile devices. Current electronic voiding log was not suitable for older adults who could not use mobile devices due to cognitive problems or vulnerabilities (
This work was supported by research fund of Chungnam National University Hospital.
No potential conflict of interest relevant to this article was reported.
The whole concept of proposed method.
Sensitivity and specificity of recognition technology monitoring urination. Each participant data (A) and the receiver operating curve (B). SROC, summary receiver operator characteristic curve; AUC, area under the curve.
Overall motion date
Variable | Coefficient | Standard error | Best |
95% CI |
---|---|---|---|---|
Bivariate | ||||
E (Loqitsensitivity) | 1.487211 | 0.2251031 | 1.046017–1.928405 | |
E (Loqitspecificity) | 2.004041 | 0.0931886 | 1.821395–2.186688 | |
Var (Loqitsensitivity) | 0.0459553 | 0.1911886 | 0.0000132–159.8106 | |
Var (Loqitspecificity) | 0.0020109 | 0.0198973 | 7.60e-12–531998.3 | |
Correlation | −1 | |||
| ||||
Bayesian summary ROC | ||||
Lambda | 5.061915 | 10.74863 | −16.00501–26.12884 | |
Theta | −1.85076 | 7.325763 | −16.20899–12.50747 | |
Beta | −1.56455 | 5.795478 | 0.797 | −12.92348–9.794379 |
| ||||
Summary | ||||
Sensitivity | 0.8156593 | 0.0338463 | 0.7400094–0.8730728 | |
Specificity | 0.8812207 | 0.0097541 | 0.8607334–0.8990477 | |
Diagnostic odd ratio | 32.82704 | 7.871636 | 20.51734–52.52214 | |
Likelihood ratio positive | 6.867018 | 0.6204718 | 5.752513–8.197451 | |
Likelihood ratio negative | 0.2091879 | 0.0383758 | 0.1460099–0.2997027 | |
Inverse likelihood ratio negative | 4.780392 | 0.8769697 | 3.33664–6.84885 |
Log likelihood=−44.984934. Number of studies=10. Covariance between estimates of E (logitsensitivity) & E (loqitspecificity)=−0.0009278.
CI, confidence interval; ROC, receiver operating characteristic.