Earticle

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 spe-cific posture and posture changes. The technology used for recognition applied the Radial Basis Function kernel-based Support Vector Ma-chine, 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 perfor-mance of the recognition technology proposed in this study. The effica-cy of smart band monitoring urination was evaluated in 10 female pa-tients 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 recogni-tion technique shows high accuracy and can be applied in clinical set-tings 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 physi-ological behaviors could become a new methodology to study human physiological behavior.

We invented a wearable device that can measure voiding time and fre-quency by checking a habitual series of characteristic motions among men. This study collected and analyzed urination time data collected smart bands worn by patients to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for assessing urination time in patients, this study aimed to explore the fea-sibility of urination management systems. This study aimed to assess urination time based on a patient’s posture and changes in posture. Motion data were obtained from a smart band on the arm. An algorithm that identifies the three stages of urination (forward movement, urina-tion, backward movement) was developed based on data collected from a 3-axis accelerometer and tilt angle data. Therefore, we analyze hidden Markov model (HMM)-based sequential data to determine uri-nation 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. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 92.5%, proving the robustness of the pro-posed 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 applying the HMM method.

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.

헬스케어 서비스는 생체신호 측정, 질병의 진단 및 예방을 위한 독자적인 다수의 서비스 플랫폼을 통해 제공할 수 있으며 인터넷, 모바일 등의 정보통신(ICT) 기술이 융합해 언제, 어디서나 이용자에게 건강 정보를 제공할 수 있어 사물 인터넷의 중심에 있다. 이에 따라 본 논문에서는 사물인터넷 기반의 배뇨관리 시스템을 설계하고 그 성능을 평가하였다. 배뇨관리 시스템 구성을 위해 저 전력의 지그비 네트워크를 구축하였으며 구현된 정전 용량형 기저귀 센서는 약 2,000시간의 동작을 확인할 수 있었다. 또한 초소형 임베디드 디바이스인 라즈베리파이를 활용하여 데이터베이스 서버를 구축하고 수집된 데이터를 저장하여 안드로이드 기반의 모바일 어플리케이션을 통해 데이터를 확인하였다. 제안된 배뇨관리 시스템은 요양병원 등 고령의 환자들을 대상으로 활용이 가능하며, 영유아를 대상으로도 편리하게 이용이 가능할 것이다.

Healthcare services can be provided through a number of independent service platforms for measurement of vital signs, diagnosis and prevention of diseases, and Information and communication technology(ICT) such as internet and mobile are converged to provide health information to users at anytime and anywhere, and it is in the center of the IoT(Internet of things). Accordingly, in this paper, we designed IoT based urination management system and evaluate the performance. A low - power Zigbee network was constructed for the configuration of the urination management system. The implemented capacitive diaper sensor was operable for the duration of 2,000 hours. We also built a database server using Raspberry Pi, a tiny embedded device, and stored the collected data to verify the data through an Android-based mobile application. The proposed urination management system can be utilized not only for the older patients, but also for the infants.