Search Results (175)

The growing demand for reliable wearable devices that can continuously monitor vital signs and track health under various conditions imposes challenging constraints on battery life. Wearable devices typically include a Photoplethysmogram (PPG) sensor, which is used for various applications such as monitoring heart rate (HR) and blood oxygenation (${\mathrm{SpO}}_2$). The efficiency of these applications depends on the quality of the PPG sensor, which acquires raw data through the analog front-end and transmits it externally. This paper presents a digital block that evaluates the quality of the PPG signal directly within the ASIC. The proposed Signal Quality Assessment (SQA) module is derived from post-processing algorithms and translated into a real-time, single-sample evaluation approach, providing significant benefits at both the sensor and system levels. The proposed solution achieves performance comparable to state-of-the-art methods, with a sensitivity of 95.2%, a specificity of 88.1%, and an accuracy of 89.52%, while introducing an extremely low energy overhead equal to $5.38$ μ$\mathrm{J}$. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Telemedicine is increasingly relevant in undergraduate medical education; however, most educational studies emphasize short-term interventions, learner satisfaction, or tele-Objective Structured Clinical Examination performance rather than evidence derived from sustained platform implementation. This multi-cohort longitudinal implementation study evaluated a multiplatform asynchronous telemedicine system integrated into simulation-based gynecology training across three consecutive academic periods at a medical simulation center in Ecuador. Platform-generated teleconsultation records were analyzed at the record level, with repeated records nested within student identifiers when students submitted more than one case. Because the expected number of submissions differed across cohorts as part of planned curricular refinements, cohort-level differences were interpreted descriptively as implementation and process indicators rather than as comparative evidence of learner performance. A total of 205 teleconsultation records from 95 student users were analyzed. Documentation quality was high for current illness documentation (98.5%), physical examination documentation (87.3%), and physiologically plausible vital signs (74.1%). Specialist responses were linked to 196/205 records (95.6%), with complete structured feedback among linked responses. Faculty expert review and learner-reported perceptions provided complementary educational evidence, including perceived usefulness of specialist feedback for gynecology learning. These findings support the feasibility of asynchronous telemedicine-supported simulation workflows and the value of platform-generated data for educational process evaluation, documentation monitoring, and feedback tracking, while not demonstrating individual competence improvement. Full article

Objectives: Food insecurity (FI) among college students is an emerging global public health concern. While the burden is international in scope, this systematic review evaluates the prevalence of FI in college populations in the United States (U.S.) and examines the suitability of commonly used FI assessment tools for this population. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted (up to April 2026) in accordance with the PRISMA 2020 Abstracts checklist. Eligible studies were peer-reviewed research articles published between 2005 and 2026, conducted in the U.S., written in English, and including college or university students with sample sizes ≥ 30. Studies were required to use validated FI assessment tools developed by the United States Department of Agriculture (USDA) or Health Watch. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal tools and only studies rated as moderate or high quality were included. Results were synthesized by grouping studies according to the FI assessment tools used. Results: Thirty studies met the inclusion criteria (total n = 213,624 students surveyed). FI prevalence among U.S. college students ranged from 14% to 72.9%. Variability in estimates was influenced by the assessment tool used, demographic characteristics, institutional settings, and regional socioeconomic differences. Shorter screening instruments, including the USDA six-Item Household Food Security Survey Module (HFSSM) Short Form and Hunger Vital Sign, demonstrated greater variability in reported FI prevalence (47% and 41%, respectively) compared with longer assessment measures. Higher FI prevalence was also more frequently reported among students of color, those from lower socioeconomic backgrounds, and female students. Conclusions: Findings demonstrate FI is prevalent among college students. Limitations of the current study include restriction to three databases, exclusion of pre-2005 studies, and inclusion of only U.S.-based studies. Variability in assessment methods, as well as consideration of confounding variables (e.g., socioeconomics, demographics and institutional settings), underscores the need for context-specific tools tailored to this population to inform effective interventions and policies globally. Full article

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management. Full article

Non-contact physiological monitoring based on remote PPG (rPPG) offers a viable alternative for the care of pediatric populations, particularly for children with cerebral palsy (CP) who present unique communication and mobility challenges. This paper presents a review of the literature on the use of rPPG for the estimation of vital signs and its application in emotional monitoring. Following the PRISMA 2020 guidelines as a methodological framework for searching and filtering, an exhaustive search was conducted in the IEEE Xplore and Scopus databases covering the period from 2017 to 2024. A total of 35 studies were selected for analysis. The review examines the evolution of rPPG algorithms—from classical mathematical approaches to recent deep-learning-based architectures—identifying critical technical challenges such as motion artifacts caused by spasticity and variations in lighting conditions. The results reveal that while rPPG has reached technical maturity for monitoring core physiological parameters such as heart rate, its application to robust emotion detection in children with CP remains limited. The main limitation identified across the surveyed literature is the critical scarcity of public or clinical datasets featuring pediatric CP cohorts. Finally, the potential of multimodal integration—combining rPPG with eye-tracking and wearable sensors—is discussed as a promising pathway toward objective emotional monitoring. Such an approach could enhance communication, support rehabilitation processes, and ultimately improve the quality of life of children with cerebral palsy and their caregivers. Full article

Background: To identify factors contributing to the low success rate of resuscitation, we optimized related links in resuscitation management and constructed a five-minute green transfer resuscitation model. Methods: A quasi-experimental pre–post quality improvement study was conducted on patients with critical and severe conditions admitted in the Department of Otorhinolaryngology at a China otolaryngology hospital. The pre-intervention group of patients were treated using the conventional resuscitation process, while the post-intervention group was treated using the “5 min green transfer” resuscitation process under the guidance of the quality improvement (QI) team. Results: The resuscitation mainly occurred in the first and second quarters, between 20:00 in the evening and 07:59 the following morning. In the pre-intervention group, the most common direct cause of initiating resuscitation was bleeding, primarily due to epistaxis, while the primary direct cause for initiating resuscitation was abnormal vital signs in the post-intervention group. The resuscitation success rate was 82.93% (34/41) in the pre-intervention group and 93.48% (43/46) in the post-intervention group. However, there was no statistically significant difference in resuscitation success rate (p = 0.14) and complication incidence (p = 0.71) between the two groups. In the pre-intervention group, six patients (14.63%) were transferred within 5 min, whereas 100% of patients (46 cases) in the post-intervention group achieved 5 min transfer, with a statistically significant difference observed between the two groups (p = 0.03). Conclusions: The intervention significantly improved the 5 min transfer efficiency, which was conducive to ensuring timely medical intervention for patients and safeguarding their clinical safety. Full article

Background and aims: The aim of this study was to examine the relationships between muscle function, dietary quality, body composition markers, and metabolic status in ambulatory patients with type 2 diabetes. The study sought to validate low-cost tools, such as handgrip strength and waist circumference, as potential “nursing vital signs” for metabolic risk stratification. Methods: A cross-sectional observational study was conducted with adult patients with type 2 diabetes. Muscle function was assessed through handgrip strength (dynamometry) and metabolic status via the HOMA-IR index. Visceral adiposity was estimated using waist circumference and the Lipid Accumulation Product (LAP); dietary quality was evaluated with the Spanish Healthy Eating Index (IASE), and cellular health through the phase angle (PhA) obtained by electrical bioimpedance. Non-parametric tests and Spearman correlations were applied due to the non-normal distribution of the data. Conclusions: In this ambulatory diabetic population, waist circumference emerged as a practical and potent surrogate for insulin resistance burden. Although metabolic dysfunction was not directly associated with dietary quality or phase angle, a high prevalence of probable sarcopenia (36.1%) and poor dietary quality (77.8%) were detected. The implementation of non-invasive tools like waist circumference and handgrip strength in nursing consultations could optimize early risk stratification and allow for more targeted lifestyle interventions. Full article

Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection capability using physiological waveforms but requires testing with emerging wearable sensor technologies for operational deployment. This study tested the Epicore Epidermal Patch for Imperceptible Care (EPIC) wearable healthcare device (WHD) for CRM-based hemodynamic monitoring during progressive central hypovolemia induced by lower-body negative pressure (LBNP) to simulate hemorrhage. Twenty participants underwent progressive LBNP while photoplethysmography (PPG) signals were recorded from EPIC sensors placed at the clavicle and triceps alongside a clinical-grade finger pulse oximeter for reference. Signal quality, heart-rate accuracy, and CRM predictions were evaluated across multiple filtering approaches. The triceps placement achieved signal quality comparable to the pulse oximeter reference when Chebyshev Type II filtering was applied, as well as high heart-rate accuracy. CRM derived from the EPIC sensor placed at the triceps tracked compensatory trends during progressive hypovolemia, but prediction magnitudes were inaccurate compared to calculated CRM values. In contrast, the clavicle placement consistently performed poorly across all measurements, regardless of the signal-processing approach. These findings support the feasibility of soft, flexible wearable sensors for continuous hemorrhage monitoring at the triceps location in operational environments where traditional finger-based pulse oximetry is impractical. Full article

Background: Recent research has highlighted the potential of postbiotics for addressing obesity and associated metabolic disorders. In this randomized, double-blind clinical trial, the efficacy of a postbiotic product in managing overweight and associated parameters was assessed. Methods: Sixty individuals were randomized into two groups: one group (n = 30) received the Postbiotic (heat-killed L. fermentum strain K8-Lb1) and the other (n = 30) a Placebo control. Body weight, waist circumference, body composition, vital signs, blood biomarkers and questionnaires for quality of life, eating behavior, eating control and gastrointestinal symptoms were assessed. Results: After a 12-week intervention, body fat mass (primary parameter) was significantly (p = 0.016) reduced in the Postbiotic group (98.15 ± 3.32% of baseline) compared to the Placebo group (100.41 ± 3.39%). In line with this, body weight (p = 0.047) and waist circumference (p = 0.034) were significantly reduced and visceral fat tended to be reduced (p = 0.053). Accordingly, the Postbiotic group tended (p = 0.066) to feel more in control of their body weight. Despite weight loss, muscle mass tended (p = 0.062) to increase. ALT, AST and GGT tended to be reduced, which may indicate an improvement in liver steatosis. Estimated average glucose (eAG) differed significantly between the groups in individuals with normal fasting glucose levels. The ability to concentrate significantly (p = 0.014) improved. Conclusions: Under an ad libitum diet, the postbiotic L. fermentum strain K8-Lb1 reduced body fat mass, body weight, and waist circumference, improved the ability to concentrate, and showed a trend towards an increase in muscle mass. The results of this pilot trial need confirmation by a pivotal trial. Full article

Remote photoplethysmography (rPPG) enables non-contact vital sign measurements using standard smart device cameras, opening up the potential of scalable health applications on consumer smart devices. However, rPPG signal quality is highly sensitive to camera sensor characteristics and image processing pipelines, which can vary between devices. This variation limits reproducibility and generalisation of rPPG-based algorithms beyond specific hardware platforms. This work presents a reproducible test environment for the validation of the camera sensor response in the context of rPPG signals. A microcontroller-driven illumination system and mechanically constrained setup are used to generate controlled, repeatable optical signals. Two characterisation tests are introduced: a time domain morphology analysis and a frequency domain attenuation analysis. Pulse timing consistency, pulse waveform morphology and normalised frequency responses are compared to assess sensor similarity. This method is applied to selected consumer devices and demonstrates consistent camera response patterns under the controlled test conditions. By explicitly addressing validation of the camera sensor and image processing pipeline, this work supports the development of more robust and transferable rPPG-based vital sign applications across a wider range of consumer devices. Full article

Accurate, non-contact vital-sign monitoring promises a scalable alternative to conventional sensors, yet low signal quality and long recording times have limited real-life adoption. We present a dual-modality system that combines Eulerian video magnified remote photoplethysmography (rPPG) from facial videos with optical flow-based shoulder tracking to estimate heart rate (HR) and respiratory rate (RR) from ultra-short 15 s recordings. With 200 participants, each providing 2 videos, 387 videos passed strict usability criteria, excluding flicker, blur, occlusion, and low illumination. For 15 s recordings, the HR estimates reached 98.5% accuracy within a ±10 beats per minute tolerance (MAE = 3.25, RMSE = 4.88, r = 0.93; p < 0.05) and the RR estimates achieved 98.4% accuracy within a ±5 respirations per minute tolerance (MAE = 0.69, RMSE = 0.87, r = 0.90; p < 0.05), exceeding prior studies that required 30 to 60 s recording lengths. Computational analysis on a standard home computer confirmed feasibility, with near real-time performance achievable on optimized hardware. By integrating complementary modalities and rigorous video quality control, the system overcomes low-SNR challenges, delivering high-fidelity, clinically validated vital signs monitoring. These results establish a robust, scalable, and precise framework for clinical and home care, demonstrating that accurate, contact-free HR and RR monitoring can now be achieved in seconds, making rapid, real-life vital signs assessment practical and accessible. Full article

Background/Objectives: Walking impairment and fatigue are common in multiple sclerosis (MS) and contribute to reduced physical function and quality of life (QoL). This study evaluated the feasibility, safety, and pre–post changes associated with a 12-week treadmill walking training (TWT) programme on walking performance, physical function, fatigue, and QoL in people with MS. Methods: Single-arm pilot study with pre–post assessments (T1–T2). Eleven adults with MS (Expanded Disability Status Scale [EDSS] ≤ 6) completed supervised TWT for 12 weeks (two 25 min sessions/week) at the Complejo Asistencial Universitario de Soria (Spain). Outcomes included SF-36, Timed Up and Go (TUG), 4 m gait speed, Short Physical Performance Battery (SPPB), and Modified Fatigue Impact Scale (MFIS). Within-participant changes were analysed using paired t-tests or Wilcoxon signed-rank tests as appropriate; effect sizes were reported as appropriate for the statistical test. Results: SF-36 total score did not change significantly (p = 0.160), while general health (p = 0.039) and vitality (p = 0.043) improved. Walking performance improved (TUG, p = 0.007; 4 m gait speed, p < 0.001), and physical function increased (SPPB, p = 0.003). Fatigue impact decreased (MFIS total, p = 0.015; physical, p = 0.007; psychosocial, p = 0.026), whereas the cognitive subscale did not change significantly (p = 0.094). Adherence was 91.7%, and no adverse events were reported. Conclusions: In this pilot sample, a 12-week TWT programme was feasible and safe and was associated with improvements in walking performance, physical function, and fatigue, with QoL changes limited to specific SF-36 domains. These findings support proceeding to a randomised controlled trial to establish efficacy. These findings should be interpreted as preliminary and exploratory, given the single-arm pre–post study design. Full article

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic health records (EHRs) from Kaohsiung Veterans General Hospital from January 2018 to December 2022 (n = 184,653). The model integrates structured variables, such as vital signs, medication and laboratory counts, and ICD-10–based comorbidity measures, with unstructured physician notes. Key physiologic measurements were transformed into binary form using clinical reference intervals, and random under-sampling addressed class imbalance. A multimodal, CNN was proposed and evaluated with an 8:2 train–test split and 10-fold Monte Carlo cross-validation. Results: The proposed model achieved a sensitivity of 0.717 (CI: [0.695, 0.738]), accuracy of 0.846 (CI: [0.842, 0.850]), and AUROC of 0.853. Binary transformation improved recall and AUROC relative to the original numeric representations. SHAP analysis showed that unstructured features dominated prediction, while structured variables added complementary value. In a small-scale pilot evaluation using the SHAP-enabled interface, participating physicians reported the system helped surface high-risk cohorts and reduced cognitive workload by consolidating relevant patient information for rapid cross-checking. Conclusions: An interpretable CNN-based clinical decision support system can predict ED revisit risk from multimodal EHR data and demonstrates practical usability in a real-world clinical setting, supporting targeted discharge planning and follow-up as a near-term approach to mitigate overcrowding. Full article

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring. Full article