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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.

Development of urination recognition technology based on Support Vector Machine using a smart band