In this research, we propose a bladder volume monitoring system that can be effectively applied for various voiding dysfunctions. Whereas conventional systems lack consecutive measurements, the proposed system can continuously monitor a user's status even during unconscious sleep. For the convenience, we design a simple and comfortable waist-belt-type device by using the body impedance analysis (BIA) technique. To support various measurement scenarios, we develop applications by connecting the device to a smartphone. To minimize motion noises, which are inevitable when monitoring over an extended period, we propose a motion artifact reduction algorithm that exploits multiple frequency sources. The experimental results show a strong relationship between the impedance variation and the bladder volume; this confirms the feasibility of our system.

Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children’s Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system’s performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.

The limited availability of calorimetry systems for estimating human energy expenditure (EE) while conducting exercise has prompted the development of wearable sensors utilizing readily accessible methods. We designed an energy expenditure estimation method which considers the energy consumed during the exercise, as well as the excess post-exercise oxygen consumption (EPOC) using machine learning algorithms. Thirty-two healthy adults (mean age = 28.2 years; 11 females) participated in 20 min of aerobic exercise sessions (low intensity = 40% of maximal oxygen uptake [VO${}_2$ max], high intensity = 70% of VO${}_2$ max). The physical characteristics, exercise intensity, and the heart rate data monitored from the beginning of the exercise sessions to where the participants’ metabolic rate returned to an idle state were used in the EE estimation models. Our proposed estimation shows up to 0.976 correlation between estimated energy expenditure and ground truth (root mean square error: 0.624 kcal/min). In conclusion, our study introduces a highly accurate method for estimating human energy expenditure during exercise using wearable sensors and machine learning. The achieved correlation up to 0.976 with ground truth values underscores its potential for widespread use in fitness, healthcare, and sports performance monitoring.

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