Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band.
- Author:
Taeg Keun WHANGBO
1
;
Sung Jong EUN
;
Eun Young JUNG
;
Dong Kyun PARK
;
Su Jin KIM
;
Chang Hee KIM
;
Kyung Jin CHUNG
;
Khae Hawn KIM
Author Information
- Publication Type:Comparative Study ; Original Article
- Keywords: Urination recognition; Urination time; Mobile voiding chart; Recurrent neural network; Smart band
- MeSH: Humans; Memory, Short-Term; Posture; Urination*; Wrist
- From:International Neurourology Journal 2018;22(Suppl 2):S91-S100
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: Though it is very important obtaining exact data about patients’ voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients’ wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management. METHODS: We designed a device capable of recognizing urination time and intervals based on specific postures of the patient and consistent changes in posture. These motion data were obtained by a smart band worn on the wrist. An algorithm that recognizes the repetitive and common 3-step behavior for urination (forward movement, urination, backward movement) was devised based on the movement and tilt angle data collected from a 3-axis accelerometer. The sequence of body movements during voiding has consistent temporal characteristics, so we used a recurrent neural network and long short-term memory based framework to analyze the sequential data and to recognize urination time. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. A comparative study was conducted between real voiding and device-detected voiding to assess the performance of the proposed recognition technology. RESULTS: The accuracy of the algorithm was calculated based on clinical guidelines established by urologists. The accuracy of this detecting device was high (up to 94.2%), proving the robustness of the proposed algorithm. CONCLUSIONS: This urination behavior recognition technology showed high accuracy and could be applied in clinical settings to characterize patients’ voiding patterns. As wearable devices are developed and generalized, algorithms detecting consistent sequential body movement patterns reflecting specific physiologic behavior might be a new methodology for studying human physiologic behavior.
