Search Results (516)

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

Modern military operations place significant physiological and cognitive demands on soldiers, necessitating innovative strategies to monitor and optimize health and performance. This narrative review examines the role of continuous physiological state monitoring and precision health strategies to enhance soldier resilience and operational readiness. Advanced wearable biosensors were analyzed for their ability to measure vital physiological parameters—such as heart-rate variability, core temperature, hydration status, and biochemical markers—in real-time operational scenarios. Emerging technological solutions, including AI-driven analytics and edge computing, facilitate rapid data interpretation and predictive health assessments. Results indicate that real-time physiological feedback significantly enhances early detection and prevention of conditions like exertional heat illness and musculoskeletal injuries, reducing medical attrition and improving combat effectiveness. However, ethical challenges related to data privacy, informed consent, and secure data management highlight the necessity for robust governance frameworks and stringent security protocols. Personalized training regimens and rehabilitation programs informed by monitoring data demonstrate potential for substantial performance optimization and sustained force readiness. In conclusion, integrating precision health strategies into military operations offers clear advantages in soldier health and operational effectiveness, contingent upon careful management of ethical considerations and data security. Full article

Sedation is an essential component of the anesthesia process. Inadequate sedation during anesthesia increases the risk of patient discomfort, intraoperative awareness, and psychological trauma. Conventional electroencephalography (EEG) based depth of anesthesia monitoring is often impractical in out-of-hospital settings due to equipment limitations and signal artifacts. Alternative non-EEG-based approaches are therefore required. In this study, we developed a machine learning model to detect inadequate sedation using 27 feature parameters, including demographics, vital signs, and heart rate variability metrics, from the open-access VitalDB database. Patient states were defined as inadequate sedation when the bispectral index (BIS) > 60. We systematically evaluated four temporal windows and four algorithms, and assessed model interpretability using Shapley Additive Explanations (SHAP). The Light Gradient Boosting Machine (LGBM) achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.825 and an accuracy (ACC) of 0.741 using a 2 s time window. Extending the time window to 20 s improved both metrics by approximately 0.012. Feature selection identified 12 key parameters that maintained comparable accuracy, confirming robustness with reduced complexity. These findings demonstrate the feasibility of using non-EEG-based physiological data for real-time detection of inadequate sedation. The developed model is interpretable, resource-efficient, scalable, and shows strong potential for integration into portable monitoring systems in prehospital, emergency, and low-resource surgical settings. Full article

The deployment of conditionally automated vehicles raises safety concerns, as drivers often engage in non-driving-related tasks (NDRTs), delaying takeover responses. This study investigates driver state monitoring (DSM) using multimodal physiological and ocular signals from the TD2D (Takeover during Distracted L2 Automated Driving) dataset, which includes synchronized electrocardiogram (ECG), photoplethysmography (PPG), electrodermal activity (EDA), and eye-tracking data from 50 participants across ten task conditions. Tasks were reassigned into three workload-based categories informed by NASA-TLX ratings. A unified preprocessing and feature extraction pipeline was applied, and 25 informative features were selected. Random Forest outperformed Support Vector Machine and Multilayer Perceptron models, achieving 0.96 accuracy in within-subject evaluation and 0.69 in cross-subject evaluation with subject-disjoint splits. Sensitivity analysis showed that temporal overlap had a stronger effect than window length, with moderately long windows (5–8 s) and partial overlap providing the most robust generalization. SHAP (Shapley Additive Explanations) analysis confirmed ocular features as the dominant discriminators, while EDA contributed complementary robustness. Additional validation across age strata confirmed stable performance beyond the training cohort. Overall, the results highlight the effectiveness of physiological and ocular measures for distraction detection in automated driving and the need for strategies to further improve cross-driver robustness. Full article

The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations. Full article

Background: The cerebral compliance (or compensatory reserve) index, RAP, is a critical yet underutilized physiological marker in the management of moderate-to-severe traumatic brain injury (TBI). While RAP offers promise as a continuous bedside metric, its broader cerebral physiological context remains partly understood. This study aims to characterize the burden of impaired RAP in relation to other key components of cerebral physiology. Methods: Archived data from 379 moderate-to-severe TBI patients were analyzed using descriptive and threshold-based methods across three RAP states (impaired, intact/transitional, and exhausted). Agglomerative hierarchical clustering, principal component analysis, and kernel-based clustering were applied to explore multivariate covariance structures. Then, high-frequency temporal analyses, including vector autoregressive integrated moving average impulse response functions (VARIMA IRF), cross-correlation, and Granger causality, were performed to assess dynamic coupling between RAP and other physiological signals. Results: Impaired and exhausted RAP states were associated with elevated intracranial pressure (p = 0.021). Regarding AMP, impaired RAP was associated with elevated levels, while exhausted RAP was associated with reduced pulse amplitude (p = 3.94 × 10⁻⁹). These two RAP states were also associated with compromised autoregulation and diminished perfusion. Clustering analyses consistently grouped RAP with its constituent signals (ICP and AMP), followed by brain oxygenation parameters (brain tissue oxygenation (PbtO₂) and regional cerebral oxygen saturation (rSO₂)). Cerebral autoregulation (CA) indices clustered more closely with RAP under impaired autoregulatory states. Temporal analyses revealed that RAP exhibited comparatively stronger responses to ICP and arterial blood pressure (ABP) at 1-min resolution. Moreover, when comparing ICP-derived and near-infrared spectroscopy (NIRS)-derived CA indices, they clustered more closely to RAP, and RAP demonstrated greater sensitivity to changes in these ICP-derived CA indices in high-frequency temporal analyses. These trends remained consistent at lower temporal resolutions as well. Conclusion: RAP relationships with other parameters remain consistent and differ meaningfully across compliance states. Integrating RAP into patient trajectory modelling and developing predictive frameworks based on these findings across different RAP states can map the evolution of cerebral physiology over time. This approach may improve prognostication and guide individualized interventions in TBI management. Therefore, these findings support RAP’s potential as a valuable metric for bedside monitoring and its prospective role in guiding patient trajectory modeling and interventional studies in TBI. Full article

Small extracellular vesicles (sEVs), typically <200 nm in diameter, have emerged as key mediators of intercellular communication, transferring bioactive molecules such as proteins, lipids, and nucleic acids between cells. This review focuses on the growing significance of sEVs in the context of sepsis, a life-threatening syndrome caused by a dysregulated immune response to infection. Sepsis remains a major global health challenge due to its complex pathophysiology, rapid progression, and the limitations of current diagnostic tools, which often fail to detect the condition early or accurately assess the host’s immune status. As interest grows in precision diagnostics, sEVs have gained attention for their potential as biomarkers in liquid biopsy—a minimally invasive approach that analyzes circulating vesicles to monitor disease. Small EVs reflect the physiological state of their cells of origin and can provide real-time insights into immune activation, inflammation, and pathogen presence. This review explores the mechanisms by which sEVs contribute to immune modulation in sepsis, recent advances in understanding their biogenesis and uptake, and their diagnostic and prognostic potential. By highlighting the role of sEVs in sepsis, we aim to underscore their promise in improving early detection, guiding therapeutic decisions, and advancing personalized medicine. Full article

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article

Chlorophylls, which are associated with carotenoids and photosynthetic protein complexes, acquire optical properties that enable the absorption of sunlight, necessary for the synthesis of energy and redox equivalents, necessary for photosynthetic absorption of CO₂ and the production of oxygen as an intermediate product. These processes are important for plants, but also for the biosphere. In stressful situations, when photosynthesis is limited, the production of reactive oxygen and other species increases, and the activation of various protective systems is necessary to remove the aforementioned reactive species or reduce the excessive reduction in photosynthetic electron transport, as the cause of the production of the reactive species. A review of studies where the content and physiological state of chlorophyll are monitored, using destructive and non-destructive methods, such as various optical methods for monitoring its content and physiological activity, is given. Polyphenolic compounds belong to non-enzymatic systems for quenching the reactive species. In addition to their presence in monomeric and oligomeric forms of polyphenols, polymerization of this type of compound can occur. In addition to having a protective effect on the plants that synthesize them, polyphenolic compounds also have a beneficial effect on the health of animals and humans who consume them from plants. Full article

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

Since extracorporeal membrane oxygenation (ECMO) is primarily used for patients in a high-risk state and is an invasive procedure, its unique application scenarios make it difficult to recruit suitable cases for clinical trials. Therefore, large animal models have become one of the most important models for preclinical evaluation of the safety and effectiveness of ECMO. This study aims to assess the safety and performance of a novel portable ECMO device with Small-tail Han sheep. Fifteen sheep were divided into a test group (LIFEMOTION, Chinabridge, Shenzhen, China) and control group (NOVALUNG XLUNG kit 230, Xonis, Heilbronn, Germany) with veno-venous ECMO (VV-ECMO) and veno-arterial ECMO (VA-ECMO) modes. Tracheal intubation, arteriovenous access, and ECMO support were performed. Vital signs and blood laboratory tests of the subjects were monitored and recorded. The main organs were examined pathologically at the end of day fourteen. The serum protein expression profile was analyzed by protein quantification techniques. All sheep were successfully weaned from ECMO without transfusion or cannula complications. No significant differences were observed between the two groups in terms of vital signs, oxygenation, hemodynamic stability, and physiological function (p > 0.05). According to the serum protein expression profile, no significant biomarkers associated with ECMO clinical complications were identified. The LIFEMOTION ECMO device demonstrated good safety and efficacy. Full article

The cognitive load of drivers directly affects the safety and practicality of advanced driving assistant systems, especially in autonomous driving scenarios where drivers need to quickly take control of the vehicle after performing non-driving-related tasks (NDRTs). However, existing driver cognitive load detection methods have shortcomings such as the inability to deploy invasive detection equipment inside vehicles and limitations to eye movement detection, which restrict their practical application. To achieve more efficient and practical cognitive load detection, this study proposes a multi-task non-contact cognitive load and physiological state estimation model based on RGB video, named CogMamba. The model utilizes multimodal features extracted from facial video and introduces the Mamba architecture to efficiently capture local and global temporal dependencies, thereby further jointly estimating cognitive load, heart rate (HR), and respiratory rate (RR). Experimental results demonstrate that CogMamba exhibits superior performance on two public datasets and shows excellent robustness under the cross-dataset generalization test. This study provides insights for non-contact driver state monitoring in real-world driving scenarios. Full article

Growing concern over cetacean welfare has highlighted the need for rigorous, science-based assessment methods. Within this context, epidermal cortisol (ECC) and oxytocin (EOC) concentrations have emerged as potentially valuable physiological indicators. In this study, we first validated the analytical measurement of ECC and EOC in bottlenose dolphins (Tursiops truncatus) using AlphaLISA assays. Subsequently, weekly ECC and EOC levels were measured over an extended period in five managed dolphins and analyzed alongside aggregated environmental and welfare-related variables, using various time lags to account for delays between physiological activity and hormone deposition in the epidermis. ECC was negatively associated with mild weight loss and diazepam administration, exhibiting seasonal variability. In contrast, EOC was negatively associated with negative welfare indicators and COVID-19 park closures but positively associated with diazepam administration and peak visitor seasons, also showing seasonal variability. However, the interpretation of EOC remains complex due to a limited understanding of the cetacean oxytocin system and its dual role in positive and negative affective states. Overall, ECC and EOC show promise as non-invasive biomarkers for monitoring long-term welfare changes in cetaceans, although further research is necessary to validate these biomarkers across broader populations and contexts and to clarify their temporal dynamics in the epidermis. Full article

Hard coal mining activity generates post-mining waste (waste rock). Waste rock is deposited in the environment in large quantities for reclamation of agricultural land. In this study, waste rock was treated as a potential source of metal pollutants. The research material (waste rock, soil, plant roots, and Lumbricidae earthworms) was obtained from sites that had been reclaimed using waste rock as well as sites without waste rock. From each site, 30 individuals (n = 30) were collected, divided into five groups, 6 individuals each. Within the group, individuals were analyzed collectively. The study tested whether selected metals (Cr, Ni, Cd, Ba, Pb, Zn, and Cu) are present in waste rock and whether they can be transferred to the soil, plant root systems, and representatives of Lumbricidae, which are important bioindicators and a source of biomarkers. Particular attention was focused on the assessment of the effects of metals deposited in situ on fatty acids in representatives of Lumbricidae and on selecting a set of fatty acids that can be used as biomarkers of physiological effects, including oxidative stress. A panel of biomarker fatty acids was used, which included a panel of 17 biomarker fatty acids from 35 fatty acids analyzed. To confirm or disprove the usefulness of the biomarker fatty acid panel in earthworms, superoxide dismutase (SOD), catalase (CAT), and thiobarbituric acid reactive substances (TBARS) were determined. The study enabled an effective comparison of reference locations with locations potentially burdened with anthropogenic sediment. The results indicate that selected metals present in the waste rock are transferred to the soil, plant root systems, and soil organisms such as Lumbricidae. Selected metals affected the lipid metabolism of Lumbricidae as stressors, leading to changes in the composition and oxidation of fatty acids. The effect on the physiological state of Lumbricidae depended on the duration of the deposit and the type of use (field, meadow, wasteland) of the land with the waste rock deposit. In earthworms obtained from sites with waste rock deposits, higher contents of biomarker saturated fatty acids and biomarker monounsaturated fatty acids and lower contents of biomarker polyunsaturated fatty acids were found compared to earthworms obtained from sites without waste rock deposits. Only Pb (lead) showed a statistically significant correlation with all analyzed parameters in earthworms obtained from sites with waste rock deposits. The results have significant practical implications for environmental protection management. The proposed set of biomarker fatty acids in Lumbricidae can be used to assess the impact of pollutants and environmental monitoring. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article