Search Results (48)

In this paper, Distribution of Relaxation Time (DRT) analysis is presented as a powerful tool for understanding the aging mechanisms in lithium-ion batteries, with a focus on its application to estimating the State of Health (SOH). A novel parameter, the characteristic relaxation time, derived from DRT analysis, is introduced to enhance SOH estimation. By analyzing the ratio of the central relaxation time (τ) between the charge transfer and diffusion peaks, the battery status can be determined without the need for historical data. Experimental data from lithium-ion batteries, including 18650 cells and LR2032 coin cells, were examined until the end of their life. Nyquist and DRT plots across various frequency ranges revealed consistent aging trends, particularly in the charge transfer and diffusion processes. These processes appeared as shifting and merging peaks in the DRT plots, signifying progressive degradation. A polynomial equation fitted to the τ ratio graph achieved a high accuracy (Adj. R² = 0.9994), enabling reliable battery lifespan prediction. Validation with a Samsung Galaxy S9+ battery demonstrated that the method could estimate its remaining life, predicting a total lifespan of approximately 2100 cycles (compared to 1000 cycles already completed). These results confirm that SOH estimation is feasible without prior data and highlight the potential of DRT analysis for accurate and quantitative prediction of battery longevity. Full article

Purpose: This study investigates trends in pediatric emergency department (ED) utilization before and after the COVID-19 pandemic, with a focus on age-specific patterns, triage severity, diagnostic categories, and clinical presentations. Methods: Data were collected for 71,560 individuals (40,428 males and 31,132 females aged 0–18 years) who visited the ED at Samsung Changwon Hospital between 1 January 2016 and 31 December 2023. Patients were categorized into pre-COVID-19 (2016–2019) and post-COVID-19 (2020–2023) periods. Age, Korean Triage and Acuity Scale (KTAS) scores, visit outcomes, diagnostic codes (ICD-10), and vital signs were analyzed. Age-specific analyses were performed in four groups: <12 months, 1–6 years, 7–12 years, and 13–18 years. Results: Since the COVID-19 pandemic, pediatric ED visits have decreased by 55.5%. The proportion of visits by infants (<12 months) and young children (1–6 years) decreased, and adolescent visits increased. Post-pandemic, there was a significant increase in lower-acuity visits (KTAS 4) and discharge rates, alongside a reduction in admissions. Visits for respiratory and infectious diseases (ICD-10 J and A & B codes) decreased markedly, and visits for non-specific symptoms (R codes) and trauma (S & T codes) increased. The mean body weight of young children increased significantly after the COVID-19 period. Conclusions: The COVID-19 pandemic has had a profound and lasting effect on pediatric emergency department utilization, with changes in the number of visits, illness patterns, and severity by age group. These findings highlight the need for age-specific strategies in emergency planning and pediatric public health policy, particularly in managing the indirect effects of pandemic-induced changes in behavior and access to healthcare. Full article

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper presents a multi-layered cybersecurity assessment of an in-house mHealth platform (PHGL-COVID), comprising a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The objective was to identify vulnerabilities across the server, mobile, and wearable components by emulating real-world attacks and conducting systematic penetration tests on each layer. Tools and methods specifically tailored to each technology were applied, revealing exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and exposure of Personal Health Records (PHRs). Key findings included incomplete container isolation, BLE metadata leakage, and persistent abuse of Android privacy permissions. This work delivers both a set of actionable recommendations for developers and system architects to strengthen the security of mHealth platforms, and a reproducible audit methodology that has been validated in a real-world deployment, effectively bridging the gap between theoretical threat models and practical cybersecurity practices in healthcare systems. Full article

Background: Digital dental photography is increasingly essential for documentation and smile design. This study aimed to compare the linear measurement accuracy of various smartphones and a Digital Single-Lens Reflex (DSLR) camera against digital models obtained by intraoral and desktop scanners. Methods: Tooth height and width from six different casts were measured and compared using images acquired with a Canon EOS 250D DSLR, six smartphone models (iPhone 13, iPhone 15, Samsung Galaxy S22 Ultra, Samsung Galaxy S23 Ultra, Samsung Galaxy S24, and Vivo T2), and digital scans obtained from the Helios 500 intraoral scanner and the Ceramill Map 600 desktop scanner. All image measurements were performed using ImageJ software (National Institutes of Health, Bethesda, MD, USA), and statistical analysis was conducted using one-way analysis of variance (ANOVA) with Tukey’s post hoc test (α = 0.05). Results: The results showed no significant differences in measurements across most imaging methods (p > 0.05), except for the Vivo T2, which showed a significant deviation (p < 0.05). The other smartphones produced measurements comparable to those of the DSLR, even at distances as close as 16 cm. Conclusions: These findings preliminary support the clinical use of smartphones for accurate dental documentation and two-dimensional smile design, including the posterior areas, and challenge the previously recommended 24 cm minimum distance for mobile dental photography (MDP). This provides clinicians with a simplified and accessible alternative for high-accuracy dental imaging, advancing the everyday use of MDP in clinical practice. Full article

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%. Full article

(1) Background: Patellar and Achilles tendon injuries have become increasingly prevalent, particularly among active populations and athletes, leading to significant functional impairments. While B-Mode ultrasound has been useful in the diagnosis of these injuries, its capacity to assess tendon mechanical properties, such as stiffness, is limited. Shear wave elastography (SWE) offers a promising alternative by measuring tissue stiffness, which may enhance the evaluation of tendon health. Previous studies have established that SWE can differentiate healthy tendons from those with pathological changes. However, reference values for specific tendon types, including the patellar and Achilles tendons, remain limited. This study aims to provide preliminary baseline SWE values for these tendons in a healthy cohort. (2) Methods: In this cross-sectional study, healthy volunteers aged 18–65, with no history of lower extremity injury, were assessed using a Samsung RS85 Prestige ultrasound system with a 14L-2 MHz transducer. SWE measurements were obtained from the patellar tendon at a single location and from the Achilles tendon at both the midportion and insertional sites. All assessments followed a standardized protocol to ensure consistency and minimize variability. (3) Results: A total of 54 healthy adult participants were included. The mean SWE value for the patellar tendon was 96.3 (SD = 10.9 kPa), with males showing significantly higher stiffness than females (99.3 kPa vs. 93.8 kPa, p = 0.009). A higher BMI was associated with lower stiffness in the patellar tendon. The mean SWE values for the Achilles tendon were 101.7 (SD = 16.2 kPa) at the insertion and 145.6 (SD = 18.8 kPa) at the midportion. (4) Conclusions: This study provides SWE values for the patellar and Achilles tendons in healthy individuals, which can serve as a foundation for future research and clinical applications. These values may help in the comparison of healthy and pathological tendons, particularly in the context of tendinopathies, tendon tears, and treatment monitoring. While shear wave elasticity shows promise as a tool for diagnosing and monitoring tendon injuries and degeneration, more research is required to establish its precise reliability and validity in clinical practice. Full article

Background: Carotid intima-media thickness (IMT) is a noninvasive surrogate marker of subclinical atherosclerosis and cardiovascular disease risk. This study explored IMT distribution across three carotid artery segments in a large cohort of healthy individuals and identified the key factors associated with increased IMT. Methods: This study utilized data from the Kangbuk Samsung Health Study, a cohort of South Korean adults aged ≥ 18 years who underwent comprehensive annual or biennial health examinations. The analysis included 86,351 healthy individuals, excluding those with known carotid disease. IMT was measured using high-resolution B-mode ultrasonography across the three segments: common carotid artery (CCA), carotid bulb, and internal carotid artery (ICA). An increased IMT was defined as a measurement of ≥1.5 mm in any segment. Multivariable linear regression analyses were conducted to identify independent predictors of increased IMT. Results: The study population had a mean age of 46.7 years and was predominantly male (69.7%). The prevalence of thickened IMT was the highest in the carotid bulb, followed by the ICA and CCA. IMT increased progressively with age and was higher in males across all segments, with the disparity becoming more pronounced after 65 years of age. The carotid bulb displayed the largest absolute IMT values, whereas the ICA exhibited a sharper age-related increment. Increased CCA IMT was strongly linked to hypertension (beta, 0.11; p < 0.001) and diabetes mellitus (beta, 0.12; p < 0.001). Both CCA and ICA IMT showed a weak but significant association with dyslipidemia (beta, 0.03; p < 0.001). Conclusions: The IMT distribution and its determinants vary across carotid segments. CCA is a robust marker of systemic vascular health, whereas the carotid bulb is the most sensitive marker for detecting early atherosclerotic changes. This study provides novel insights into segment-specific IMT patterns and their association with cardiovascular risk factors in a large, healthy Asian population. Full article

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems. Full article

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation. Full article

Sex differences in the effect of prolonged sitting time on anxiety symptoms have not yet been explored. This study examined the sex-specific association between prolonged sitting time and anxiety prevalence in Korean adults. Community-dwelling adults aged >18 years who underwent a cross-sectional structured study survey of physical activity and mental health tests were enrolled as part of the Kangbuk Samsung Hospital Cohort Study from 2012 to 2019. The prevalence of anxiety was evaluated using the Clinically Useful Anxiety Outcome Scale (CUXOS) questionnaire. The mean daily sitting time was 7.9 ± 3.4 h in men and 6.8 ± 3.6 h in women. After adjustments for possible confounding factors, the adjusted mean CUXOS score was the highest in participants sitting for ≥10 h, followed by 5–9 h, and <5 h, in that order. In the post-hoc Bonferroni analysis, there were significant differences in the adjusted mean CUXOS scores in group comparisons. A multivariate logistic regression analysis was conducted after adjusting for potential confounding factors. A prolonged sitting time was positively associated with an increased prevalence of anxiety in both men and women, with stronger associations in women than in men. It is necessary to prevent anxiety by adjusting or reducing sitting time in adults, especially women. Full article

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions. Full article

Background/Objectives: Smoking cessation is acknowledged for its health benefits. However, it paradoxically increases diabetes mellitus (DM) risk shortly after quitting due to weight gain. This research aimed to investigate how smoking status could affect the development of DM, focusing on how the risk of acquiring diabetes changed over time after quitting smoking, independent of variables such as weight gain. Methods: The data of 386,558 participants of the Kangbuk Samsung Health Study, excluding those with pre-existing DM, were examined. Smoking status and its long-term effects on DM risk were assessed using multivariate Cox proportional hazards models. Lifestyle factors, including weight change, physical activity levels, and alcohol intake, were adjusted as time-varying covariates throughout the follow-up period. Results: Modified hazard ratios (HRs) indicated no notable disparity in DM risk between individuals who previously smoked and those who had never smoked (HR: 1.04, 95% CI: 0.999–1.08, p-value < 0.001). In contrast, current smokers exhibited a significantly increased DM risk (HR: 1.29, 95% CI: 1.24–1.35, p-value < 0.001). Within the first six years post-cessation, former smokers initially faced a higher DM risk than never smokers (0–2 years, HR: 1.22, 95% CI: 1.15–1.31, p-value < 0.001; 3–5 years, HR: 1.11, 95% CI: 1.04–1.20, p-value < 0.001). After 12 years, they realigned with never smokers (12–46 years, HR: 0.92, 95% CI: 0.86–0.98, p-value = 0.002). Current smokers consistently showed a higher DM risk (0–9 years, HR: 1.29, 95% CI: 1.14–1.46, p-value < 0.001). Adjusting for covariates such as weight change and physical activity did not alter these findings. Conclusions: Our results indicated that former smokers initially experienced an elevated risk of DM relative to never smokers. This increased risk aligned with the risk of never smokers after six years, and the risk continued to improve after 12 years compared to never smokers. This contrasted with current smokers, who maintained a heightened risk of DM, even when adjustments were made for weight change, physical activity, and alcohol intake as time-varying covariates. Full article

Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit. Full article

Background: Rehabilitation programs for children with cerebral palsy (CP) aim to improve their motor and cognitive skills through repeated and progressively challenging exercises. However, these exercises can be tedious and demotivating, which can affect the effectiveness and feasibility of the programs. To overcome this problem, virtual reality VR-assisted exergaming has emerged as a novel modality of physiotherapy that combines fun and motivation with physical activity. VR exergaming allows children with CP to perform complex movements in a secure and immersive environment, where they can interact with virtual objects and scenarios. This enhances their active engagement and learning, as well as their self-confidence and enjoyment. We aim to provide a comprehensive overview of the current state of research on VR exergaming for CP rehabilitation. The specific objectives are: To identify and describe the existing studies that have investigated the effects of VR exergaming on motor function and participation outcomes in children with CP. In addition, we aim to identify and discuss the main gaps, challenges, and limitations in the current research on VR exergaming for CP rehabilitation. Finally, we aim to provide recommendations and suggestions for future research and practice in this field. Methods: In June 2023, we conducted a systematic search on Scopus, Web of Science, PubMed, Cochrane, and Embase for randomized trials and cohort studies that applied VR-assisted exergaming to rehabilitating patients with CP. The inclusion criteria encompassed the following: (1) Randomized controlled trials (RCTs) and cohort studies involving the rehabilitation of children with CP; (2) the application of VR-based exergaming on the rehabilitation; (3) in comparison with conventional rehabilitation/usual care. The quality of the selected RCTs was evaluated using Cochrane’s tool for risk of bias assessment bias includes. Whereas the quality of cohort studies was assessed using the National Institutes of Health (NIH) tool. Results: The systematic search of databases retrieved a total of 2576 studies. After removing 863 duplicates, 1713 studies underwent title and abstract screening, and 68 studies were then selected as eligible for full-text screening. Finally, 45 studies were involved in this review (n = 1580), and 24 of those were included in the quantitative analysis. The majority of the included RCTs had a low risk of bias regarding study reporting, participants’ attrition, and generating a random sequence. Nearly half of the RCTs ensured good blinding of outcomes assessors. However, almost all the RCTs were unclear regarding the blinding of the participants and the study personnel. The 2020 retrospective cohort study conducted at Samsung Changwon Hospital, investigating the effects of virtual reality-based rehabilitation on upper extremity function in children with cerebral palsy, demonstrated fair quality in its methodology and findings. VR-assisted exergaming was more effective than conventional physiotherapy in improving the Gross Motor Function Measurement (GMFM)-88 score (MD = 0.81; 95% CI [0.15, 1.47], p-value = 0.02) and the GMFM walking and standing dimensions (MD = 1.45; 95% CI [0.48, 2.24], p-value = 0.003 and MD = 3.15; 95% CI [0.87, 5.42], p-value = 0.007), respectively. The mobility and cognitive domains of the Pediatric Evaluation of Disability Inventory score (MD = 1.32; 95% CI [1.11, 1.52], p-value < 0.001) and (MD = 0.81; 95% CI [0.50, 1.13], p-value < 0.0001) were also improved. The Canadian Occupational Performance Measure performance domain (MD = 1.30; 95% CI [1.04, 1.56], p-value < 0.001), the WeeFunctional Independence Measure total score (MD = 6.67; 95% CI [6.36, 6.99], p-value < 0.0001), and the Melbourne Assessment of Unilateral Upper Limb Function-2 score (p-value < 0.001) improved as well. This new intervention is similarly beneficial as conventional therapy in improving other efficacy measures. Conclusions: Our findings suggest that VR-assisted exergaming may have some advantages over conventional rehabilitation in improving CP children’s functioning and performance in daily life activities, upper and lower limb mobility, and cognition. VR-assisted exergaming seems to be as effective as conventional physiotherapy in the other studied function measures. With its potential efficacy, better feasibility, no reported side effects, and entertaining experience, VR-assisted exergaming may be a viable complementary approach to conventional physiotherapy in rehabilitating children with CP. Full article

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise. Full article