Initial outcomes using a novel bedwetting alarm (Gogoband®) that utilizes real time artificial intelligence to wake users prior to wetting

Franco, Israel; Coble, Jon

doi:10.1016/j.jpurol.2023.04.024

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Implementing data augmentation techniques tailored for NE could mitigate this imbalance, ultimately enhancing the accuracy of NE moment estimation. Although there are several predictive approaches for NE patients [ 9 , 10 , 49 , 50 ], we believe that our work has a novelty compared to these works. Franco and Coble [ 50 ] proposed a novel bedwetting alarm system utilizing real-time heart rate variability (HRV) analysis and machine learning in order to wake users before wetting occurs.…”

Section: Resultsmentioning

confidence: 99%

“…Although there are several predictive approaches for NE patients [ 9 , 10 , 49 , 50 ], we believe that our work has a novelty compared to these works. Franco and Coble [ 50 ] proposed a novel bedwetting alarm system utilizing real-time heart rate variability (HRV) analysis and machine learning in order to wake users before wetting occurs. However, as we discussed above, a prediction based on a single data source may totally fail in cases where the data source is not available because of several issues including device failure and the loosened wearing of the device.…”

Section: Resultsmentioning

confidence: 99%

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Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Lee,

Moon,

Lee

et al. 2024

Sensors

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Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Lee,

Moon,

Lee

et al. 2024

Sensors

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Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Ahmad,

Khondker,

Kwong

et al. 2024

Curr Treat Options Peds

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Improving the efficacy of enuresis alarm treatment through early prediction of treatment outcome: a machine learning approach

Jönsson,

Andersson,

Nevéus

et al. 2023

Front. Urol.

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IntroductionBedwetting, also known as enuresis, is the second most common chronic health problem among children and it affects their everyday life negatively. A first-line treatment option is the enuresis alarm. This method entails the child being awoken by a detector and alarm unit upon urination at night, thereby changing their arousal mechanisms and potentially curing them after 6–8 weeks of consistent therapy. The enuresis alarm treatment has a reported success rate above 50% but requires significant effort from the families involved. Additionally, there is a challenge in identifying early indicators of successful treatment.MethodsThe alarm treatment has been further developed by the company Pjama AB, which, in addition to the alarm, offers a mobile application where users provides data about the patient and information regarding each night throughout the treatment. The wet and dry nights are recorded, in addition to the actual timing of the bedwetting incidents. We used the machine learning model random forest to see if predictions of treatment outcome could be made in early stages of treatment and shorten the evaluation time based on data from 611 patients. This was carried out by using and analyzing data from patients who had used the Pjama application. The patients were split into training and testing groups to evaluate to what extent the algorithm could make predictions every day about whether a patient’s treatment would be successful, partially successful, or unsuccessful.ResultsThe results show that a large number of patient outcomes can already be predicted accurately in the early stages of treatment.DiscussionAccurate predictions enable the correct measures to be taken earlier in the treatment, including increasing motivation, adding pharmacotherapy, or terminating treatment. This has the potential to shorten the treatment in general, and to detect patients who will not respond to the treatment early on, which in turn can improve the lives of children suffering from enuresis. The results show great potential in making the treatment of enuresis more efficient.

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Initial outcomes using a novel bedwetting alarm (Gogoband®) that utilizes real time artificial intelligence to wake users prior to wetting

Cited by 3 publications

References 9 publications

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Improving the efficacy of enuresis alarm treatment through early prediction of treatment outcome: a machine learning approach

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