Na Hyun Seok, Kim Khae Hawn
Department of Urology, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Korea.
Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, Sejong, Korea.
J Exerc Rehabil. 2021 Aug 23;17(4):287-292. doi: 10.12965/jer.2142474.237. eCollection 2021 Aug.
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.
本研究的目的是通过开发一种基于智能手环的算法来识别女性的排尿间隔，从而探索排尿管理系统的可行性。我们设计了一种基于患者特定姿势和姿势变化来识别排尿时间和间隔的设备。用于识别的技术应用了基于径向基函数核的支持向量机，这是一种通过同时判断复杂学习数据的特征来促进多维分析的教学方法。为了评估所提出的识别技术的性能，我们比较了实际排尿情况和设备感知到的排尿情况。进行了一项实验来评估本研究中提出的识别技术的性能。在10名没有排尿问题的女性患者中评估了智能手环监测排尿的效果。整个实验共进行了3天。参与者的平均年龄为28.73岁（26 - 34岁），没有排尿困难的迹象。算法的最终准确率是根据泌尿科医生的临床指南计算得出的。实验显示平均准确率高达91.0%，证明了所提出算法的稳健性。这种排尿行为识别技术具有很高的准确率，可应用于临床环境中以表征女性患者的排尿模式。随着可穿戴设备的发展和日益普及，检测反映特定生理行为的特定连续身体运动模式的算法可能会成为研究人类生理行为的一种新方法。
