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Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
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Development of a voiding diary using urination recognition technology in mobile environment.

Gun Hyun Park1, Su Jin Kim2, Young Sam Cho3

  • 1Department of Urology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.

Journal of Exercise Rehabilitation
|January 18, 2021
PubMed
Summary
This summary is machine-generated.

A new wearable device accurately measures male urination time and frequency using motion data. This smart band algorithm offers a robust solution for managing voiding habits, achieving 92.5% accuracy.

Keywords:
Mobile voiding chartUrination management systemUrination time recognition

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Area of Science:

  • Biomedical Engineering
  • Urology
  • Wearable Technology

Background:

  • Traditional voiding charts are inconvenient and prone to inaccuracies.
  • Objective measurement of urination patterns is clinically significant for men's health.
  • Existing methods for monitoring voiding behavior lack real-time, automated capabilities.

Purpose of the Study:

  • To develop and validate a wearable device-based algorithm for measuring male urination time and frequency.
  • To assess the feasibility of using smart band technology for automated urination management systems.
  • To determine urination time by analyzing posture and posture changes using motion data.

Main Methods:

  • Utilized a smart band to collect motion data (3-axis accelerometer, tilt angle).
  • Developed a hidden Markov model (HMM)-based algorithm to identify three urination stages: forward movement, urination, and backward movement.
  • Analyzed real-time signal data against the HMM model to calculate urination time and compared with clinical guidelines.

Main Results:

  • Achieved an average accuracy of 92.5% in recognizing urination time.
  • Demonstrated the robustness of the algorithm using acceleration and tilt angle data.
  • Successfully identified distinct stages of the urination process via the smart band.

Conclusions:

  • The proposed wearable device and HMM algorithm provide a highly accurate method for measuring male urination time.
  • This technology offers a feasible and non-invasive approach for urination management systems.
  • Smart band-based motion analysis represents a significant advancement in objective urological monitoring.