Development of Personalized Urination Recognition Technology Using Smart Bands.
- Author:
Sung Jong EUN
1
;
Taeg Keun WHANGBO
;
Dong Kyun PARK
;
Khae Hawn KIM
Author Information
- Publication Type:Original Article
- Keywords: Urination Recognition; Urination Monitoring System; Mobile Voiding Chart
- MeSH: Acceleration; Arm; Humans; Posture; Urination*; Vibration
- From:International Neurourology Journal 2017;21(Suppl 1):S76-S83
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. METHODS: This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. RESULTS: 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. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. CONCLUSIONS: The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.
