Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

• Main Authors: Sung-Jong Eun, Jun Young Lee, Han Jung, Khae-Hawn Kim
• Format: Article
• Language: English
• Published: Korean Continence Society 2021-09-01
• Series: International Neurourology Journal
• Subjects: urinary patient; urination recognition; urination management system; mobile voiding chart; long short-term memory; recurrent neural network
• Online Access: http://www.einj.org/upload/pdf/inj-2142276-138.pdf
• ISSN: 2093-4777; 2093-6931

Purpose In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. Methods We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. Results Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. Conclusions This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.