Pd21-11 the Dynamic Urine Vibration Halter: A New Outpatient Device for Remote Patient Monitoring of Uroflow

Blaivas, Jerry G.; Benedon, Matthew; Weinberger, James; Rozenberg, Yosi; Ravid, Lior; Vapnek, Jonathan M.

doi:10.1016/j.juro.2015.02.1440

Cited by 3 publications

(2 citation statements)

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“…It is reported that the low visiting percentage of urinary disorder patients might be attributed to the lack of a convenient and private measurement of voiding dysfunction [ 5 ]. In [ 6 ], a small-sized vibration sensor that transforms vibration signals into uroflow during urination was proposed. The device was designed to assist in in-home monitoring of voiding status of patients.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Urine Flow Velocity Using Millimeter-Wave FMCW Radar

Kong

Kim

2022

Sensors

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This study investigated the feasibility of remotely estimating the urinary flow velocity of a human subject with high accuracy using millimeter-wave radar. Uroflowmetry is a measurement that involves the speed and volume of voided urine to diagnose benign prostatic hyperplasia or bladder abnormalities. Traditionally, the urine velocity during urination has been determined indirectly by analyzing the urine weight during urination. The maximum velocity and urination pattern were then used as a reference to determine the health condition of the prostate and bladder. The traditional uroflowmetry comprises an indirect measurement related to the flow path to the reservoir that causes time delay and water waves that impact the weight. We proposed radar-based uroflowmetry to directly measure the velocity of urine flow, which is more accurate. We exploited Frequency-Modulated Continuous-Wave (FMCW) radar that provides a range-Doppler diagram, allowing extraction of the velocity of a target at a certain range. To verify the proposed method, first, we measured water speed from a water hose using radar and compared it to a calculated value. Next, to emulate the urination scenario, we used a squeezable dummy bladder to create a streamlined water flow in front of the millimeter-wave FMCW radar. We validated the result by concurrently employing the traditional uroflowmetry that is based on a weight sensor to compare the results with the proposed radar-based method. The comparison of the two results confirmed that radar velocity estimation can yield results, confirmed by the traditional method, while demonstrating more detailed features of urination.

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Section: Introductionmentioning

confidence: 99%

Estimation of Urine Flow Velocity Using Millimeter-Wave FMCW Radar

Kong

Kim

2022

Sensors

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“…Similarly, because of data acquisition difficulties, this study targeted men needing assistive monitoring of voiding dysfunction to serve as participants. Jerry Blaivas et al [11] attached a small-size vibration sensor that transforms vibration signals into uroflow during urination to the dossal site of the penis. The device assists in in-home monitoring of patients' voiding status.…”

Section: Introductionmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

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