We invented a wearable device that can measure the voiding time and number by checking an on-going series of characteristic motion of men. This study collected and analyzed urination time data sensed through smart bands worn by patients to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination time in patients, this study aimed to explore the feasibility of urination management systems.

This study aimed to develop the recognizes urination time based on a patient’s posture and changes in position. Motion data was obtained from a smart band on the arm. An algorithm that identifies the three stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and tilt angle data because the features used for analyzing sequential data that has temporal characteristics. So we analyze HMM(Hidden Markov Model)-based subsequent data and provide a way to recognize urination time. Real-time data were acquired from the smart band. For data corresponding to a specific duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination.

An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. Interim analysis of smart medical devices for urinary diagnosis in our latest project shows that human behavior patterns using wearable devices are very reliable and accurate. For 15 participants, the overall diagnostic odds ratio compared to the urinary 3-days urinary diary is 25.03 (95% CI; 17.57, 35.65). The pooled sensitivity and specificity is 0.79 (95% CI; 0.73, 0.84) and  0.87 (95% CI; 0.85, 0.89). The experiment showed a high average accuracy of 92.5%, proving the robustness of the proposed algorithm.

The proposed urination time recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after apply the HMM method. Urination time recognition technology suggested in the present study might apply to monitor voiding patterns in real life. In this study, we used the voiding habits of men during urination to monitor the voiding pattern. Thus, there are some limitations because voiding habits could be different among the male population.

We proposed a new urination time recognition technology to evaluate the voiding pattern in real life instead of conventional voiding diary.

Int Neurourol J 2018;22(Suppl 2):S76-82