Objective.Guzik's and Porta's indices of heart rate asymmetry (HRAGI, HRAPI) quantify different aspects of R-R interval (RRI) dynamics. Although highly correlated, the two indices are not interchangeable and may provide partially independent information. To enable a direct and interpretable comparison between them, a connecting bridge is required.Approach.To investigate whether a magnitude-based index exists that aligns closely with the number-based HRAPIand, if so, how this index differs from the magnitude-based HRAGI, we analyze 5 min RRI recordings obtained from 24 male and 29 female participants of different ages during spontaneous breathing, as well as from 14 young participants performing controlled breathing at 0.1 Hz and 0.25 Hz. The mean (Md, Ma) and standard deviation (SDd, SDa) of beat-to-beat deceleration and acceleration magnitudes were computed. Wang's index of heart rate asymmetry (HRAWI) was defined as the percentage of Md relative to the sum of Md and Ma, whereas HRAGIincorporates both the mean and the SD of these magnitudes.Main results.Across all conditions, Bland-Altman plots showed that the differences between HRAWIand HRAPI(-0.06 ± 1.01%) exhibited narrower limits of agreement with mean difference close to zero. The slopes of the regression lines for HRAWIversus HRAPIdid not differ significantly from the slope of the line of identity. HRAGIwas significantly greater than HRAWIduring 0.1 Hz breathing, but not during 0.25 Hz breathing, paralleling increases in both the Md and SDd during slow breathing. At the same time, the ratio SDd/Md was consistently greater than SDa/Ma, in accordance with the mathematical formulations underlying HRAGIand HRAWI.Significance.The magnitude-based index HRAWIclosely matches the number-based HRAPIacross a range of physiological conditions. Difference between HRAGIand HRAWIis primarily determined by disparities in the SD/M ratio of RRI decelerations relative to accelerations.

This study aims to investigate the impact of localized ventricular ischemia on cardiac electrical activity using a three-dimensional computational modeling framework, and to evaluate the robustness of relative ECG-based quantitative markers under varying ischemic conditions. A three-dimensional heart torso model was developed incorporating anisotropic myocardial conductivity and rule-based fiber orientation in the ventricular regions to further enhance physiological plausibility while preserving computational efficiency. Electrical propagation was modeled using modified FitzHugh-Nagumo equations within a bidomain formulation. Localized ischemia was introduced through controlled alterations in intracellular conductivity, resting membrane potential, and ionic kinetics. Simulations were performed under healthy and ischemic conditions, and ECG signals were analyzed using both qualitative waveform assessment and normalized quantitative metrics, including ST/QRS and T/QRS ratios. A sensitivity analysis was conducted by varying ischemia severity, location, and size. Model outputs were compared with clinical ECG signals from the European ST-T Database using correlation and RMSE after normalization and temporal alignment. The simulations revealed clear lead-dependent ECG alterations associated with ischemia, including ST-segment deviations and T-wave modifications. The ST/QRS ratio showed consistent sensitivity to ischemia severity while remaining relatively stable across variations in ischemic size and location. Quantitative comparison with clinical ECG signals demonstrated moderate agreement, with good agreement of the QRS complex and larger discrepancies in repolarization features. This study highlights the importance of incorporating anisotropic conduction and fiber orientation in computational ECG modeling. The proposed framework provides a reproducible and computationally efficient approach for investigating ischemia-induced ECG changes and supports the use of relative ECG metrics as robust descriptors for methodological analysis and synthetic data generation.

Objective.Wearable facial expression recognition requires sensing modalities that are unobtrusive, illumination-independent, and compatible with everyday eyewear form factors. Existing optical and electromyographic approaches are limited by line-of-sight requirements, skin contact, or restricted action unit (AU) coverage. This work investigates the feasibility of non-contact capacitive sensing for facial muscle activity monitoring within the strict geometric constraints of standard eyewear.Approach.We developed FERGlasses, integrating eight loading-mode capacitive channels via a custom flexible PCB and active shielding to detect skin-to-frame displacement. A validation study with 40 participants was conducted to elicit seven facial action coding system-defined AUs. Signal fidelity was assessed using independent Random Forest regressors and benchmarked against commercial optical-proximity (OCOsense) and computer-vision (Py-Feat) standards.Main results.The system demonstrated high agreement for upper-face activity, achieving median cosine similarities of 0.97 for the Outer Brow Raiser (AU02) and 0.87 for the Nose Wrinkler (AU09), while lower-face actions such as the Chin Raiser (AU17) yielded a median of 0.33, consistent with mechanical signal attenuation along the frame. Channel-ablation analysis quantified the specific anatomical contributions of each electrode group, identifying eyebrow and rhinion sensors as primary signal drivers for upper-face and perinasal actions, respectively, and temple sensors as critical for cheek raiser detection.Significance.This study provides the first metrological validation of multi-channel capacitive sensing for AU-level recognition in eyewear. The results establish the modality as a robust, privacy-preserving, and illumination-independent alternative to optical methods for longitudinal physiological monitoring, with the current prototype serving as a wired research platform and full consumer-grade integration identified as a future engineering objective.

Objective. Warm-up is a fundamental part of the training session and competition preparation, improving performance and reducing sports injuries. The FIFA 11+ is a specific evidence-based routine created to enhance neuromuscular performance and prevent lower-limb injuries. Infrared thermography (IRT) is a non-invasive tool for monitoring tissue state and thermoregulation responses. This study examined the acute effects of the FIFA 11+ warm-up on skin surface temperature (Tsk) patterns of the dominant lower limb in amateur football players using IRT.Approach. A pre-post observational design was applied to 120 amateur players (60 men, 60 women) before a match. Baseline and post-intervention Tsk measurements were acquired with a FLIR T540-EST camera following the TERMOINEF protocol.Main results. Significant post-warm-up Tsk reductions were detected in proximal muscle regions, particularly in quadriceps and adductors, with a reduction of -1.9 to -2.4 °C (ES = - 1.63 to -1.92, large) in women and -0.7 to -1.2 °C (ES = - 0.66 to -1.07, moderate) in men. Conversely, distal regions such as the anterior plantar arch showed marked Tsk increases of +2.6 °C (ES = 1.83, large) in women; +2.1 °C (ES = 1.42, large) in men. Men exhibited higher absolute Tsk values overall (η2≈ 0.17-0.26), whereas women displayed greater relative percentage changes, including sex-specific Achilles tendon response (a decrease in women versus a slight increase in men).Significance. FIFA 11+ induces heterogeneous, region- and sex- dependent thermal adaptations, supporting the use of IRT as a valid tool for individualized warm-up monitoring and optimization in football.

Objective.Photoplethysmography (PPG) and remote PPG (rPPG) are widely used non-invasive techniques for monitoring cardiovascular parameters. However, signal artifacts from motion, lighting variations, and environmental noise pose significant challenges for accurate physiological measurement, particularly in non-contact rPPG systems. To address these issues, we propose a novel generative inpainting framework designed to restore corrupted segments of PPG and rPPG signals.Approach.Our method leverages a large-scale synthetic dataset that spans a broad range of heart rates (30-180 bpm) and incorporates diverse artifact profiles to simulate real-world conditions. The inpainting model is built upon a custom Wasserstein GAN architecture, using a gradient penalty to ensure stable adversarial training. This selective reconstruction approach targets only the corrupted segments while preserving the integrity of high-quality signal portions.Main results.Results show that the proposed framework improves signal quality compared to corrupted signals. For synthetic datasets spanning heart rates from 30 to 180 bpm, signal-to-noise ratio increases from approximately -0.65 to -0.20 dB to 3.27-4.16 dB after inpainting, while the mean absolute error decreases from 0.08-0.09 to 0.05-0.06. Feature-level similarity also improves, with Fréchet Encoder Distance reduced from 0.12 to 0.03 for real PPG and from 0.07 to 0.01 for real rPPG, and consistent reductions observed across all synthetic heart-rate ranges (from 0.23-0.47 to 0.01-0.04). Heart-rate estimates derived from the reconstructed signals are statistically equivalent to those obtained from clean references.Significance.The proposed generative inpainting framework effectively restores degraded PPG and rPPG signals and preserves heart-rate estimates, supporting its use in non-critical physiological monitoring applications such as wellness monitoring and automotive contexts. Validation on real data was limited to relatively clean, resting-state recordings; further studies are required to assess performance under high-motion and real-world conditions.

Objective.Continuous blood pressure (BP) monitoring is crucial for detecting nocturnal hypertension and acute hemodynamic changes. Conventional cuff-based methods disrupt sleep and miss transient BP fluctuations from sleep-related events or instability. Cuffless methods, such as pulse transit time (PTT), offer potential but often struggle to reliably track acute BP fluctuations due to complex and nonlinear hemodynamics.Approach.We developed a capacitive sensing pad system incorporating PTT with additional hemodynamic features for unobtrusive, continuous BP monitoring in supine subjects. The pad contains ultra-sensitive single-electrode capacitive (SEC) sensors made from carbon nanotube composites. In passive contact, SEC sensor on the back of the chest captures intrathoracic blood volume changes through deep tissue permittivity and ballistocardiography, while another under the leg detects arterial pulse-induced tissue vibrations. PTT is derived from the temporal delay between chest and leg signals. A neural network model incorporates PTT and intrathoracic blood volume features to improve BP estimation.Main results.In human trials (N= 30) with arm-cuff reference, the system showed strong correlation (r⩾ 0.94), with a mean error (ME) ⩽ 0.1 mmHg and standard deviation (SD) ⩽ 5.6 mmHg. In a subset (N= 8) with continuous finger-cuff reference, it maintained strong correlation (r⩾ 0.89), with a ME ⩽ 0.2 mmHg and SD ⩽ 7.8 mmHg.Significance.These results suggest the possibility of bed-based unobtrusive and continuous BP monitoring in the supine position.

Objective.Individual differences across subjects reduce the accuracy of physiological signal-based cuffless blood pressure (BP) estimation. However, training a personalized model with a large amount of data is impractical. This study aims to learn a personalized model using only a few labeled samples (e.g. 5 datapoints).Approach.This study introduced a two-stage training method to enhance the few-shot personalized model with self-supervised learning. In the first training stage, self-supervised learning is used to learn shared features from physiological signals across subjects. In the second stage, few-shot learning is used to adapt the model to each subject based on the pre-trained encoder from the first stage.Main result.Experiments were conducted on the PulseDB dataset. Under the 5-shot setting, the proposed method achieved mean absolute errors of 6.57 ± 6.22 mmHg and 3.66 ± 3.99 mmHg for systolic BP (SBP) and diastolic BP (DBP) estimation, respectively, when using photoplethysmogram (PPG) and electrocardiogram. Using only PPG signals, the method achieved 6.77 ± 6.43 mmHg and 3.80 ± 3.92 mmHg for SBP and DBP estimation, respectively. The proposed approach exceeded previous non-personalized and transfer learning methods. Its generalization capability was validated on two additional smaller datasets, demonstrating the generalization ability of the proposed method.Significant.Overall, the proposed method provides a new approach for few-shot personalization of cuffless BP estimation models, which is helpful for accurate and individualized BP estimation.

Objective.Seismocardiography (SCG) contains rich physiological information about the structure and function of the heart, providing a new dimension for early screening and dynamic monitoring of cardiovascular diseases. However, SCG is relatively weak, susceptible to severe external interference, even has strong individual differences in morphology, making it difficult for traditional algorithms to accurately detect AO, AC and other core features.Approach.Herein, Combining the time-frequency joint distribution characteristics of SCG, we improve a novel adaptive extended Kalman filter (KF) algorithm for accurate AO and AC detection. To achieve unified modeling for different individual SCG, Gaussian mixture module is used to fit the morphological template in the time-frequency domain based on the optimal estimation strategy. Then, in order to balance the estimation accuracy and computational efficiency of the nonlinear system constructed based on SCG, the extended KF is constructed by linearizing the state transition equation. Moreover, with the aim of accurately estimating the time-varying noise components in motion, the forgetting factorαkis introduced based on the residualekwith the low-pass filtering strategy to adaptively update the measurement noise covariance matrixR, thereby achieving high-quality filtering to SCG with the AO and AC area.Main results.In addition, the experiment is conducted on the open-source CEBS dataset, indicating that the proposed algorithm has better filtering effect and higher AO and AC' detection accurate on the static SCG. Furthermore, the portable hardware system is designed for collecting SCG during 6 min walk test. Meanwhile, the impedance cardiography equipment is employed to record heart rate, left ventricular ejection time and other hemodynamics parameters. Compared with these common algorithms, the proposed algorithm also has better detection performance on the SCG during exercise.Significance.In the future, the proposed algorithm will be integrated with the portable SCG hardware system designed, which is expected to be applied in the convenient diagnosis of heart diseases, the dynamic measurement of cardiovascular parameters, the dynamic blood pressure measurement without the need for wearing cuffs and more medical scene.

Objective.To develop a comprehensive physiological dataset for assessing internal and external stress and to propose robust automated stress recognition methods based on photoplethysmographic (PPG) signals.Approach.We established the Internal and External Stress Dataset (IESD), comprising PPG signals from 107 participants subjected to four distinct stress-inducing paradigms. Exploratory analyses revealed significant differences in heart rate variability (HRV) across these paradigms, underscoring the necessity for advanced methods capable of differentiating various stress types. To address this, we introduced a transfer learning-based inter-paradigm stress recognition model utilizing a domain adversarial neural network combined with maximum mean discrepancy for robust feature extraction.Main results.Analysis identified significant differences between internal and external stress, as well as among different external paradigms. Our proposed model demonstrated superior accuracy in recognizing homologous stress compared to heterologous stress within the same target domain, achieving accuracies of 73.86% (TSST to ST) and 60.41% (TSST to VWT). Moreover, the deep feature extraction significantly improved recognition performance and robustness across both intra- and inter-paradigm contexts.Significance.This study provides a valuable dataset and advanced methodology to enhance automated stress detection capabilities, effectively differentiating internal and external stress. The application of deep learning significantly improves recognition accuracy, offering promising prospects for future research and practical applications in stress monitoring.

Objective.Bioimpedance spectroscopy (BIS) has emerged as a promising technique for screening cervical intraepithelial neoplasia (CIN) since the electrical properties vary with the pathological status of cervical tissues. In this study, we aimed to evaluate the ability of CIN screening using multiple features extracted from BIS measurements collected with a multi-electrode BIS probe.Approach.This study enrolled 161 patients with gynecological diseases, including 44 with and 117 without cervical dysplasia. Upon the histological diagnosis, the samples were classified as normal, CIN I, and CIN II with p16 positive (p16(+))/CIN III. Complex impedance spectra ofin vitrocervical conization tissues were measured using the BIS probe. A Cole-Cole plot was generated from each patient's data measured on the conized cervix, and various features were extracted. Receiver operating characteristic (ROC) curves were generated, and the area under each ROC curve (AUC) was calculated.Main results.As a result, fifteen features from Cole-Cole plots differed significantly (p<0.01) between normal cervices and CIN. The AUCs based on multiple features, as determined by multivariable logistic regression, were 0.93 for normal cervix vs CIN I, 0.99 for normal cervix vs CIN II p16(+)/CIN III, and 0.94 for normal cervix vs CIN. These AUCs were improved by 14.8%, 7.6%, and 8.0%, respectively, compared with the results based on features extracted from only the real part of the impedance spectra.Significance.In conclusion, CIN can be accurately diagnosed using multiple features extracted from the impedance spectrum ofin vitrocervical samples. Particularly, this method was highly accurate in classifying CIN II p16(+)/CIN III, which has a higher risk of progression to cancer.