Search Results (839)

Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a critical challenge as current NI-FECG methods struggle to extract high SNR FECG signals and conventional algorithms fail when signal quality deteriorates. We proposed a U-Net-based method that enables robust R-peak detection directly from low-SNR FECG signals (0–12 dB), bypassing the need for high-SNR inputs that are clinically difficult to acquire. The method was evaluated on both real (A&D FECG) and synthetic (FECGSYN) databases, comparing against ten state-of-the-art detectors. The proposed method significantly reduces false predictions compared to commonly used detection algorithms, achieving a PPV of 99.81%, an SEN of 100.00%, and an F1-score of 99.91% on the A&D FECG database and a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94% on the FECGSYN database. Further investigation of robustness in low-SNR conditions (0 dB, 5 dB, and 10 dB) achieved 87.38% F1-score at 0 dB SNR on real signals, surpassing the best-performing algorithm implemented in Neurokit by 13.58%. In addition, the algorithm showed ≤2.65% performance variation across tolerance windows (50 reduced to 20 ms), further underscoring its detection accuracy. Overall, this work reduces the reliance on high-SNR FECG signals by reliably extracting R-peaks from suboptimal signals, providing implications for the reliability of fetal heart rate variability analysis in real-world noisy environments. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Background and Objectives: Heart failure (HF) represents a major public health challenge worldwide, with rising prevalence, high morbidity and mortality rates, and substantial healthcare costs. Non-invasive telemonitoring has emerged as a promising adjunct in HF management, yet its clinical effectiveness remains unclear. Materials and Methods: In this systematic review, we summarize randomized controlled trials (RCTs) between 2004 and 2024 examining the efficacy of non-invasive telemonitoring on mortality, readmission, and quality of life (QoL) in HF. In addition, we characterize the heterogeneity of features of different telemonitoring interventions. Results: In total, 32 RCTs were included, comprising 13,294 participants. While some individual studies reported benefits, non-invasive telemonitoring demonstrated mixed effects on mortality, readmission rates, and QoL. The most common modality for interfacing with patients was by mobile application (53%), followed by web portals (22%), and stand-alone devices (19%). Periodic feedback (63%) was more common than continuous feedback (31%) or on-demand feedback (6%). Clinician reviews of patient telemonitoring data was event-triggered (44%) more commonly than based on a prespecified timeline (38%). In most designs (90%), patients played a passive role in telemonitoring. Conclusions: Non-invasive telemonitoring interventions for HF exhibited considerable variation in duration and system design and had a low rate of patient engagement. Future work should focus on identifying telemonitoring-responsive subgroups and refining telemonitoring strategies to complement traditional HF care. Full article

The awareness of the importance of monitoring human vital signs has increased recently due to the outbreak of the COVID-19 pandemic. Non-invasive heart rate monitoring devices, in particular, have become some of the most popular tools for health monitoring. However, heart rate data alone are not enough to reflect the health of one’s cardiovascular function or arterial health. This growing interest has spurred research into developing high-fidelity non-invasive pulse waveform sensors. These sensors can provide valuable information such as data on blood pressure, arterial stiffness, and vascular aging from the pulse waveform. Among these sensors, optical fiber sensors (OFSs) stand out due to their remarkable properties, including resistance to electromagnetic interference, capability in monitoring multiple vital signals simultaneously, and biocompatibility. This paper reviews the latest advancements in using OFSs to measure human vital signs, with a focus on pulse waveform analysis. The various working mechanisms of OFSs and their performances in measuring the pulse waveform are discussed. In addition, we also address the challenges faced by OFSs in pulse waveform monitoring and explore the opportunities for future development. This technology shows great potential for both clinical and personal non-invasive pulse waveform monitoring applications. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

The luminous environment, shaped by daylight and electric light, significantly influences visual performance, physiological responses, and perceptual experiences. While these light sources are often perceived as distinct due to their differing effects on occupants’ cognition and well-being, the underlying mechanisms remain unclear. Nine lighting conditions were evaluated, combining three spectral types—daylight (DL), conventional LED (CLED), and daylight LED (DLED)—with three horizontal illuminance levels (300 lx, 500 lx, and 1000 lx). Twelve healthy subjects completed visual performance tasks (2-back working memory test), physiological measurements (heart rate variability and critical flicker frequency), and subjective evaluations. The results revealed that 500 lx consistently yielded the most favorable outcomes: 2-back task response speed improved by 6.2% over 300 lx and 1000 lx, and the critical flicker frequency difference was smallest, indicating reduced fatigue. DLED lighting achieved cognitive and physiological levels comparable to daylight. Heart rate variability analyzes further confirmed higher alertness levels under 500 lx DLED lighting (LF/HF = 3.31). Subjective ratings corroborated these findings, with perceived alertness and comfort highest under DLED and 500 lx conditions. These results demonstrate that DLED, which offers a balanced spectral composition and improved uniformity, may serve as an effective lighting configuration for supporting both visual and non-visual performance in indoor settings lacking daylight. Full article

Academic self-pressure is a significant source of stress for students, with physiological and cognitive implications that can influence academic performance. This study investigated the impact of academic self-pressure on heart rate variability (HRV) and cognitive performance, exploring the moderating role of physical activity through an experimental intervention. A randomized controlled trial (RCT) was conducted on a sample of 50 secondary school students, divided into an experimental group and a control group. The intervention, lasting 16 weeks, integrated physical activity based on escape room challenges with the traditional school curriculum. The results show that the experimental group recorded a significant improvement in HRV, a reduction in perceived stress, and an increase in cognitive performance, working memory, and academic achievement. Correlational and regression analyses highlighted the key role of physiological stress regulation in academic success. The findings emphasize the potential of integrating movement-based learning activities, such as escape room interventions, into school curricula as an effective strategy to enhance students’ stress regulation, executive functioning, and academic performance. By improving physiological self-regulation and cognitive efficiency, this approach supports a more holistic educational model that addresses both academic outcomes and student well-being. These results suggest that incorporating physically active, cognitively engaging tasks into the classroom may foster resilience, motivation, and adaptive coping skills, offering practical value for educational policy and classroom practice. Full article

Background/Objectives: Obesity is increasingly common among patients with acute ST-segment elevation myocardial infarction (STEMI), potentially influencing both clinical evaluation and outcomes. Traditional echocardiographic metrics may be suboptimal for prognosis estimation in this population. Left ventricular myocardial work (LVMW) represents an emerging, load-adjusted marker of myocardial performance. This study aimed to assess the prognostic relevance of LVMW in obese STEMI patients. Methods: A total of 143 patients presenting with STEMI were prospectively enrolled and categorized based on their obesity status (body mass index ≥30 kg/m²). LVMW parameters were measured using echocardiography within 72 ± 24 h of hospital admission. The patients were monitored for major adverse cardiovascular events (MACE), defined as cardiovascular death, malignant ventricular arrhythmias, or unplanned hospitalizations due to heart failure or acute coronary syndrome. Results: During a median follow-up of 13 months (interquartile range: 6–28 months), MACE occurred in 30 patients (21%). Among obese individuals, left ventricular global work efficiency (LVGWE) emerged as the most robust predictor of adverse events, with an area under the receiver operating characteristic curve of 0.736 (95% confidence interval [CI]: 0.559–0.914; p = 0.009). A threshold value of 79% for LVGWE was identified as optimal for predicting MACE. Kaplan–Meier analysis revealed significantly lower event rates in obese patients with LVGWE ≥79% (log-rank p = 0.006). In univariate Cox regression analysis, LVGWE <79% was associated with a markedly elevated risk of MACE in obese patients (hazard ratio [HR] = 5.59; 95% CI: 1.33–23.50; p = 0.019), and remained a significant predictor in the overall cohort (HR = 2.73; 95% CI: 1.26–5.90; p = 0.010). Conclusions: LVGWE demonstrates strong prognostic utility in STEMI, particularly among obese patients. The incorporation of myocardial work indices into routine evaluation may enhance risk stratification and guide management in this high-risk subgroup. Full article

Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. Full article

Background: To explore the differences in exercise capacity between the extracardiac conduit (ECC) and lateral tunnel (LT) Fontan. Methods: 2169 patients (36% LT (n = 774); 64% ECC (n = 1395)) underwent a Fontan operation between 2000 to 2023 in a multi-institutional Fontan registry. LT patients were age-matched to ECC patients, and cardiopulmonary exercise test (CPET) results were compared. Following age-matching and exclusion of those without CPET data, 470 patients emerged with 235 LT and 235 ECC patients. Results: ECC achieved higher peak heart rates (174 vs. 169 bpm, p = 0.0008) and heart rates at ventilatory anaerobic threshold (VAT) (130 vs. 119 bpm p = 0.0005). Oxygen saturations at peak (93.0 vs. 90.0%, p = 0.0003) and baseline (95 vs. 92.5%, p < 0.0001) were higher in the ECC group. The VO₂ at VAT was higher in the ECC (17.8 vs. 16.4 mL/kg/min p = 0.0123). Baseline pre-exercise heart rate, peak oxygen pulse, VE/VCO₂ slope, peak VO₂, peak % of predicted VO₂, peak work rate, and peak % of predicted work rate were similar (all, p > 0.05). Notably, less than 35% of the cohort had a documented CPET. Conclusions: We found that the ECC performed statistically better on many parameters of exercise capacity, including the ability to increase heart rate, have higher peak and baseline saturations, and to achieve superior VO₂ at VAT. However, the magnitude of difference was small, suggesting that the translational value into the clinical realm may be limited. With a minority of the registry patients having CPET completed, this illuminates the need for the implementation of CPET surveillance for Fontan patients. Full article

Background: This study aimed to investigate the neurocardiovascular responses during an Active Stand (AS) test, utilizing both pre-processed and raw signals, to predict adverse health outcomes including orthostatic intolerance (OI) during the AS, and future falls and mortality. Methods: A total of 2794 participants from The Irish Longitudinal Study on Ageing (TILDA) were included. Continuous cardiovascular (heart rate (HR), systolic (sBP), and diastolic (dBP) blood pressure) and near infra-red spectroscopy-based neurovascular (tissue saturation index (TSI), oxygenated hemoglobin (O₂Hb), and deoxygenated hemoglobin (HHb)) signals were analyzed using Statistical Parametric Mapping (SPM) to identify significant group differences across health outcomes. Results: The results demonstrated that raw (unprocessed) signals, particularly O₂Hb and sBP/dBP, were more effective in capturing significant physiological differences associated with mortality and OI compared to pre-processed signals. Specifically, for OI, raw sBP and dBP captured significant changes across the entire test, whereas pre-processed signals showed intermittent significance. TSI captured OI only in its pre-processed form, at approximately 10 s post-stand. For mortality, raw O₂Hb was effective throughout the AS test. No significant differences were observed in either pre-processed or raw signals related to falls, suggesting that fall risk may require a multifactorial assessment beyond neurocardiovascular responses. Conclusions: These findings highlight the potential utility of raw signal analysis in improving risk stratification for OI and mortality, with further studies needed to validate these findings and refine predictive models for clinical applications. This study underscores the importance of retaining raw data for certain physiological assessments and provides a foundation for future work in developing machine-learning models for early health outcome detection. Full article

Background: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. Methods: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. Results: The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. Conclusions: In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models. Full article

Background and Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the degeneration of both upper and lower motor neurons, leading to the rapid decline of motor function. In recent years, dysfunction of the autonomic nervous system (ANS) has also been increasingly recognized as a contributing factor in various neurodegenerative diseases, including ALS. This study is the second publication from our ALS research cohort at Kaunas Clinics. Our previous work examined ultrasonographic changes in the phrenic nerve as a supplementary diagnostic approach for ALS. Materials and Methods: In the present study, we investigated ultrasonographic alterations of the vagus nerve within the same ALS cohort, aiming to explore correlations with ANS involvement. We performed high-resolution ultrasonography of the vagus nerve (VN), collected clinical data, conducted heart rate monitoring, and evaluated respiratory function. Results: We prospectively included 32 ALS patients meeting “Gold Coast” criteria and 64 age- and sex-matched control patients. The average onset of ALS was 57.97 ± 9.22 years, and the duration of the disease was15.41 ± 9.04 months. For ALS patients, we found significantly reduced vagus nerve cross-sectional area (CSA) at the level of the carotid artery bifurcation bilaterally compared to controls (right VN 1.86 ± 0.21 vs. 2.07 ± 0.18 mm², p < 0.001; left VN 1.69 ± 0.21 vs. 1.87 ± 0.21 mm², p < 0.001). Reduced values of the left VN positively correlated with the reduced values of FEV1% and sO₂. Conclusions: Our findings revealed a significant bilateral reduction in vagus nerve size in ALS patients compared to controls, suggesting that vagal atrophy may serve as a potential marker of autonomic dysfunction in ALS. Full article

In response to the escalating complexity and frequency of wildland fires, this study investigates the feasibility of using wearable devices for real-time monitoring of cardiac, respiratory, physical, and environmental parameters during live wildfire suppression tasks. Data were collected from twelve male firefighters (FFs) from the Italian National Fire Corp during a simulated protocol, including rest, running, and active fire suppression phases. Physiological and physical metrics such as heart rate (HR), heart rate variability (HRV), respiratory frequency (f_R) and physical activity levels were extracted using chest straps. The protocol designed to mimic real-world firefighting scenarios revealed significant cardiovascular and respiratory strain, with HR often exceeding 85% of age-predicted maxima and sustained elevations in high-stress roles. Recovery phases highlighted variability in physiological responses, with reduced HRV indicating heightened autonomic stress. Additionally, physical activity analysis showed task-dependent intensity variations, with debris management roles exhibiting consistently high exertion levels. These findings demonstrate the relevance of wearable technology for real-time monitoring, providing an accurate analysis of key metrics to offer a comprehensive overview of work-rest cycles, informing role-specific training and operational strategies. Full article

The ST segment of an ECG signal is a feature that changes in the event of cardiac ischemia, a condition that is an early warning sign of myocardial infarction. Being able to monitor this feature in real time would be highly beneficial for preventing recurrent heart attacks. However, to be worn daily, such a monitoring device must be extremely miniaturized, down to the scale of a single integrated circuit. Currently, it is possible to integrate a heart rate detector, but, to our knowledge, no existing work presents a chip capable of detecting ST segment deviation. This is mainly because accurate ST segment measurement requires low-distortion signal processing, as specified in the International Electrotechnical Commission (IEC) standard. At the same time, the system is required to filter out baseline wander, whose frequency components may partially overlap with those of the ST segment. In this study, we relied on wavelet-based analysis and reconstruction to compare several wavelet types. We optimized their hyperparameters to minimize implementation complexity while satisfying the low-distortion constraints. We also propose an ASIC-oriented architecture and evaluate its post-layout performance in terms of area and power consumption. The post-layout results indicate that the Daubechies wavelet db3 offers the best trade-off among the evaluated configurations. It exhibits an area utilization of $1.18$ mm² and a post-layout power consumption of $4.89$ $\mathsf{\mu }$W, while preserving the ST segment in compliance with the IEC standard, thanks in particular to its effective baseline wandering filtering of $6.9$ dB. These results demonstrate the feasibility of embedding automatic ST segment extraction on-chip. Full article