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

Abstract

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.

Conclusion: 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.

Keywords: Long short-term memory; Mobile voiding chart; Recurrent neural network; Urinary patient; Urination management system; Urination recognition.

Fig. 1.

Conceptual diagram of the urination management system. Adapted from Whangbo et al. Int Neurourol J 2018;22(Suppl 2):S91-100 [20].

Fig. 2.

Recognizing the patient’s urinary activity.

Fig. 3.

Implementation screens of the intelligent patient management system. RNN, recurrent neural network; LSTM, long short-term memory.

Fig. 4.

Receiver operating characteristic comparison of each methods. HMM, hidden Markov model; SVM, support vector machine.