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Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.

This research was designed to evaluate if it is viable to awaken children with urinary incontinence at the pre-void phase using a smart wearable device and enable them to control incontinence with fine-tuned individual parameters determined by the device intelligently. To address this research question, a miniaturised wearable smart device was built in this multidisciplinary research to monitor the non-linear behaviours of the bladder during its expansion with urine intake. The device, with its customisable abilities, sets an individual alarm point to awaken the child with incontinence before voiding. Safety parameters, aesthetics and ergonomic use of the device were investigated through hospital trials with children and the device was improved based on the obtained feedback from these trials.Clinical Relevance: The device will help children learn how to control their incontinence over time.

Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.

This research was designed to evaluate if it is viable to awaken children with urinary incontinence at the pre-void phase using a smart wearable device and enable them to control incontinence with fine-tuned individual parameters determined by the device intelligently. To address this research question, a miniaturised wearable smart device was built in this multidisciplinary research to monitor the non-linear behaviours of the bladder during its expansion with urine intake. The device, with its customisable abilities, sets an individual alarm point to awaken the child with incontinence before voiding. Safety parameters, aesthetics and ergonomic use of the device were investigated through hospital trials with children and the device was improved based on the obtained feedback from these trials.Clinical Relevance: The device will help children learn how to control their incontinence over time.

A smart, wearable, medical device -- the so-called ``MyPAD'' which monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting -- i.e., treating Nocturnal Enuresis (NE) at the pre-void stage, was developed in this research. Patient and public engagement was an integral part of each development phase, particularly with respect to human factors, in collaboration with physicians, radiologists, psychologists, nurses, engineers and designers. Feedback was obtained from real-world trials and further improvements were incorporated into the device. Clinical Relevance: MyPAD will treat the NE efficiently against wetness-based post-void alarms and will enable children to gain control of incontinence over time.

Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.

A smart, wearable, medical device -- the so-called ``MyPAD'' which monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting -- i.e., treating Nocturnal Enuresis (NE) at the pre-void stage, was developed in this research. Patient and public engagement was an integral part of each development phase, particularly with respect to human factors, in collaboration with physicians, radiologists, psychologists, nurses, engineers and designers. Feedback was obtained from real-world trials and further improvements were incorporated into the device. Clinical Relevance: MyPAD will treat the NE efficiently against wetness-based post-void alarms and will enable children to gain control of incontinence over time.

A smart, wearable, medical device -- the so-called ``MyPAD'' which monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting -- i.e., treating Nocturnal Enuresis (NE) at the pre-void stage, was developed in this research. Patient and public engagement was an integral part of each development phase, particularly with respect to human factors, in collaboration with physicians, radiologists, psychologists, nurses, engineers and designers. Feedback was obtained from real-world trials and further improvements were incorporated into the device. Clinical Relevance: MyPAD will treat the NE efficiently against wetness-based post-void alarms and will enable children to gain control of incontinence over time.

Computer vision techniques have been used extensively to automatically monitor human activities; however, applications for analysing animal behaviour are sparse. The analysis of bat behaviour in particular has attracted only one or two studies. Most existing work uses either expensive thermal imaging equipment, or bespoke sensors which are not accessible to field researchers, ecologists, and scientists studying behaviour. The work we present here uses spectral analysis techniques to quantify wingbeat frequency, using a single imaging device in low-light. We propose two modified techniques based on bounded box metrics, and similarity matrices, for measuring periodic and cyclical motion as a 1D time domain signal. These are transformed to the frequency domain using Short Time Fourier Transform (STFT). Finally we evaluate these techniques against the baseline algorithm proposed by Cutler and Davis 5, using expert-annotated ground-truth data. © 2013 Springer-Verlag.

Computer vision techniques have been used extensively to automatically monitor human activities; however, applications for analysing animal behaviour are sparse. The analysis of bat behaviour in particular has attracted only one or two studies. Most existing work uses either expensive thermal imaging equipment, or bespoke sensors which are not accessible to field researchers, ecologists, and scientists studying behaviour. The work we present here uses spectral analysis techniques to quantify wingbeat frequency, using a single imaging device in low-light. We propose two modified techniques based on bounded box metrics, and similarity matrices, for measuring periodic and cyclical motion as a 1D time domain signal. These are transformed to the frequency domain using Short Time Fourier Transform (STFT). Finally we evaluate these techniques against the baseline algorithm proposed by Cutler and Davis [5], using expert-annotated ground-truth data.