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. 2020 May;58(5):943-965.
doi: 10.1007/s11517-019-02091-x. Epub 2020 Feb 24.

Intelligent autonomous treatment of bedwetting using non-invasive wearable advanced mechatronics systems and MEMS sensors : Intelligent autonomous bladder monitoring to treat NE

Affiliations

Intelligent autonomous treatment of bedwetting using non-invasive wearable advanced mechatronics systems and MEMS sensors : Intelligent autonomous bladder monitoring to treat NE

Kaya Kuru et al. Med Biol Eng Comput. 2020 May.

Abstract

Post-void alarm systems to monitor bedwetting in nocturnal enuresis (NE) have been deemed unsatisfactory. The aim of this study is to develop a safe, comfortable and non-invasive pre-void wearable alarm and associated technologies using advanced mechatronics. Each stage of development includes patient and public involvement and engagement (PPI). The early stages of the development involved children with and without NE (and parents) who were tested at a hospital under the supervision of physicians, radiologists, psychologists, and nurses. The readings of the wearable device were simultaneously compared with B-mode images and measurements, acquired from a conventional ultrasound device, and were found to correlate highly. The results showed that determining imminent voiding need is viable using non-invasive sensors. Following on from "proof of concept," a bespoke advanced mechatronics device has been developed. The device houses custom electronics, an ultrasound system, intelligent software, a user-friendly smartphone application, bedside alarm box, and a dedicated undergarment, along with a self-adhesive gel pad-designed to keep the MEMS sensors aligned with the abdomen. Testing of the device with phantoms and volunteers has been successful in determining bladder volume and associated voiding need. Five miniaturised, and therefore more ergonomic, versions of the device are being developed, with an enabled connection to the cloud platform for location independent control and monitoring. Thereafter, the enhanced device will be tested with children with NE at their homes for 14 weeks, to gain feedback relating to wearability and data collection involving the cloud platform. Graphical Abstract Design of the MyPAD advanced mechatronics system.

Keywords: Advanced mechatronics; Bedwetting; Bladder; MEMS; Nocturnal enuresis; Ultrasound; Wearable health monitoring devices.

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Figures

Graphical Abstract
Graphical Abstract
Design of the MyPAD advanced mechatronics system
Fig. 1
Fig. 1
Design of the MyPAD advanced mechatronics system
Fig. 2
Fig. 2
The bed-side alarm box (left) and the box painted by the children (right)
Fig. 3
Fig. 3
Components of the jig test device
Fig. 4
Fig. 4
Devices used to test the techniques and approaches
Fig. 5
Fig. 5
Empty bladder: First trial for the volunteer MP1
Fig. 6
Fig. 6
1/2 full bladder: Third trial for the volunteer MP1
Fig. 7
Fig. 7
Full bladder: Fifth trial for the volunteer MP1
Fig. 8
Fig. 8
a Height and b width values of the bladder during the expansion from an empty status to a full status for 6 children in 20-min intervals. The bladder width and height increase as the bladder expands with urine
Fig. 9
Fig. 9
a Bladder wall thickness and b distance between the sensor and bladder during the expansion from an empty status to a full status for 6 children in 20-min intervals. The thickness of the wall decreases whereas the anterior wall approaches to the abdomen as the bladder expands
Fig. 10
Fig. 10
Full bladder images of 6 children from MP1 to MP6
Fig. 11
Fig. 11
Volume expansion of the bladder: Diagonal red dashed line corresponds to the linear regression of the bladder expansion with respect to time; yellow vertical dashed line indicates the point where the expansion starts, red vertical dashed line shows the point where the voiding need starts for MP1, MP5, and MP6 (7–9-year-old children) whereas the orange vertical dashed line corresponds to the voiding need triggering point for MP2 (14-year-old child)
Fig. 12
Fig. 12
Illustration of the ideal bladder sensing location
Fig. 13
Fig. 13
Design concept for a wearable support garment. a General framework of the concept. b Positioning with respect to bladder. c Representation with respect to morphologic types: (i) the devices shape in relation to the pubic region, this will be graded to fit population types. (ii) The garment will provide tension to the rear of the device maintaining contact with the skin. (iii) The inner pocket of the proposed garment has a window which enables the self-adhesive gel pad which is adhered to the body side of the device to protrude through and adhere to the skin
Fig. 14
Fig. 14
The undergarment along with the space model and the sticky gel pad to be used with the sensors
Fig. 15
Fig. 15
The components of the bespoke device: 1 TiePie, 1 custom electronics and US system, 5 BNC to SMA cables, 1 trigger cable, 1 BNC to BNC cable, 2 USB cables, 1 USB hub, 1 power supply, 4 receivers (EX) and 1 transmitter (Tx)
Fig. 16
Fig. 16
Connection of the system components
Fig. 17
Fig. 17
Sensors placed in the 3D print. Each probe could also be at a different angle, essentially focusing them to different depths
Fig. 18
Fig. 18
The design of the five probes printed on a flexible film based on the space model
Fig. 19
Fig. 19
Flexible films on which sensors are printed
Fig. 20
Fig. 20
Components of the the bed-side alarm box
Fig. 21
Fig. 21
The data acquisition interface
Fig. 22
Fig. 22
The data saved in the CSV format: First column is the time in nanosecond (ns) and the other four columns are the channel outputs in Volts
Fig. 23
Fig. 23
Determination of bladder status and control of the undergarment placement
Fig. 24
Fig. 24
a Phantom and the test design of sensors on the phantom, and b each channel corresponds to the echoed pulses acquired from each probe. The composite graph is an averaged signal form all four channels
Fig. 25
Fig. 25
Echoed pulses acquired from the full bladder and their composite presentation
Fig. 26
Fig. 26
Sensors placed in the 3D print acquiring echoed pulses from the bladder
Fig. 27
Fig. 27
MyPAD AMS within the edge and cloud platforms

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