Hasil untuk "Physiology"

Shuyang Chu, Menghan Xia, Mengyao Yuan et al.

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings.

Yaowen Zhang, Libera Fresiello, Peter H. Veltink et al.

Continuous blood pressure (BP) estimation via photoplethysmography (PPG) remains a significant challenge, particularly in providing comprehensive cardiovascular insights for hypertensive complications. This study presents a novel physiological model-based neural network (PMB-NN) framework for BP estimation from PPG signals, incorporating the identification of total peripheral resistance (TPR) and arterial compliance (AC) to enhance physiological interpretability. Preliminary experimental results, obtained from a single healthy participant under varying activity intensities, demonstrated promising accuracy, with a median standard deviation of 6.88 mmHg for systolic BP and 3.72 mmHg for diastolic BP. The median error for TPR and AC was 0.048 mmHg*s/ml and -0.521 ml/mmHg, respectively. Consistent with expectations, both estimated TPR and AC exhibited a reduction as activity intensity increased.

Matthias Jammot, Björn Braun, Paul Streli et al.

Understanding affect is central to anticipating human behavior, yet current egocentric vision benchmarks largely ignore the person's emotional states that shape their decisions and actions. Existing tasks in egocentric perception focus on physical activities, hand-object interactions, and attention modeling - assuming neutral affect and uniform personality. This limits the ability of vision systems to capture key internal drivers of behavior. In this paper, we present egoEMOTION, the first dataset that couples egocentric visual and physiological signals with dense self-reports of emotion and personality across controlled and real-world scenarios. Our dataset includes over 50 hours of recordings from 43 participants, captured using Meta's Project Aria glasses. Each session provides synchronized eye-tracking video, headmounted photoplethysmography, inertial motion data, and physiological baselines for reference. Participants completed emotion-elicitation tasks and naturalistic activities while self-reporting their affective state using the Circumplex Model and Mikels' Wheel as well as their personality via the Big Five model. We define three benchmark tasks: (1) continuous affect classification (valence, arousal, dominance); (2) discrete emotion classification; and (3) trait-level personality inference. We show that a classical learning-based method, as a simple baseline in real-world affect prediction, produces better estimates from signals captured on egocentric vision systems than processing physiological signals. Our dataset establishes emotion and personality as core dimensions in egocentric perception and opens new directions in affect-driven modeling of behavior, intent, and interaction.

Ba-Thinh Nguyen, Thach-Ha Ngoc Pham, Hoang-Long Duc Nguyen et al.

Remote photoplethysmography (rPPG) is an emerging contactless physiological sensing technique that leverages subtle color variations in facial videos to estimate vital signs such as heart rate and respiratory rate. This non-invasive method has gained traction across diverse domains, including telemedicine, affective computing, driver fatigue detection, and health monitoring, owing to its scalability and convenience. Despite significant progress in remote physiological signal measurement, a crucial characteristic - the intrinsic periodicity - has often been underexplored or insufficiently modeled in previous approaches, limiting their ability to capture fine-grained temporal dynamics under real-world conditions. To bridge this gap, we propose Reperio-rPPG, a novel framework that strategically integrates Relational Convolutional Networks with a Graph Transformer to effectively capture the periodic structure inherent in physiological signals. Additionally, recognizing the limited diversity of existing rPPG datasets, we further introduce a tailored CutMix augmentation to enhance the model's generalizability. Extensive experiments conducted on three widely used benchmark datasets - PURE, UBFC-rPPG, and MMPD - demonstrate that Reperio-rPPG not only achieves state-of-the-art performance but also exhibits remarkable robustness under various motion (e.g., stationary, rotation, talking, walking) and illumination conditions (e.g., nature, low LED, high LED). The code is publicly available at https://github.com/deconasser/Reperio-rPPG.

Fabian Stolp, Charlotte Brandebusemeyer, Franziska Hradilak et al.

Integrated development environments (IDE) support developers in a variety of tasks. Unobtrusively capturing developers' cognitive load while working on different programming tasks could help optimize developers' work experience, increase their productivity, and positively impact code quality. In this paper, we propose a study in which the IntelliJ-based IDE plugin CognitIDE is used to collect, map, and visualize software developers' physiological activity data while they are working on various software development tasks. In a feasibility study, participants completed four simulated everyday working tasks of software developers - coding, debugging, code documentation, and email writing - based on Java open source code in the IDE whilst their physiological activity was recorded. Between the tasks, the participants' perceived workload was assessed. Feasibility testing showed that CognitIDE could successfully be used for data collection sessions of one hour, which was the most extended duration tested and was well-perceived by those working with it. Furthermore, the recorded physiological activity indicated higher cognitive load during working tasks compared to baseline recordings. This suggests that cognitive load can be assessed, mapped to code positions, visualized, and discussed with participants in such study setups with CognitIDE. These promising results indicate the usefulness of the plugin for diverse study workflows in a natural IDE environment.

Chuang Ma, Yu Pei, Jianhang Zhang et al.

Micro-expressions (MEs) are subtle, fleeting nonverbal cues that reveal an individual's genuine emotional state. Their analysis has attracted considerable interest due to its promising applications in fields such as healthcare, criminal investigation, and human-computer interaction. However, existing ME research is limited to single visual modality, overlooking the rich emotional information conveyed by other physiological modalities, resulting in ME recognition and spotting performance far below practical application needs. Therefore, exploring the cross-modal association mechanism between ME visual features and physiological signals (PS), and developing a multimodal fusion framework, represents a pivotal step toward advancing ME analysis. This study introduces a novel ME dataset, MMME, which, for the first time, enables synchronized collection of facial action signals (MEs), central nervous system signals (EEG), and peripheral PS (PPG, RSP, SKT, EDA, and ECG). By overcoming the constraints of existing ME corpora, MMME comprises 634 MEs, 2,841 macro-expressions (MaEs), and 2,890 trials of synchronized multimodal PS, establishing a robust foundation for investigating ME neural mechanisms and conducting multimodal fusion-based analyses. Extensive experiments validate the dataset's reliability and provide benchmarks for ME analysis, demonstrating that integrating MEs with PS significantly enhances recognition and spotting performance. To the best of our knowledge, MMME is the most comprehensive ME dataset to date in terms of modality diversity. It provides critical data support for exploring the neural mechanisms of MEs and uncovering the visual-physiological synergistic effects, driving a paradigm shift in ME research from single-modality visual analysis to multimodal fusion. The dataset will be publicly available upon acceptance of this paper.

Tao Zhou, Lingyu Shu, Zixing Zhang et al.

Deep learning has shown great promise in physiological signal analysis, yet its progress is hindered by heterogeneous data formats, inconsistent preprocessing strategies, fragmented model pipelines, and non-reproducible experimental setups. To address these limitations, we present Tyee, a unified, modular, and fully-integrated configurable toolkit designed for intelligent physiological healthcare. Tyee introduces three key innovations: (1) a unified data interface and configurable preprocessing pipeline for 12 kinds of signal modalities; (2) a modular and extensible architecture enabling flexible integration and rapid prototyping across tasks; and (3) end-to-end workflow configuration, promoting reproducible and scalable experimentation. Tyee demonstrates consistent practical effectiveness and generalizability, outperforming or matching baselines across all evaluated tasks (with state-of-the-art results on 12 of 13 datasets). The Tyee toolkit is released at https://github.com/SmileHnu/Tyee and actively maintained.

Madison McGraw, Amod Sharma, Dinesh Bhattarai et al.

Abstract Ischemia‐reperfusion injury (IRI) is the central contributing factor to acute kidney injury (AKI). Kidney tissue that becomes necrotic during this process releases a variety of pro‐inflammatory factors, driving activation of the complement cascade. Complement 5 (C5), in particular, has become an important therapeutic target, yet pharmacologic targeting does not achieve complete inhibition nor target all variants of this abundant protein. Here, we have generated and characterized a novel rat model of CRISPR/Cas9‐mediated global C5 deletion (C5−/−). C5−/− rats displayed no differences in growth, blood chemistry, or kidney morphology/function from wild‐type (C5+/+) counterparts at baseline. Subsequently, we compared C5−/− rats to C5+/+ littermates in a renal IRI model to assess differences in the post‐injury response. Compared to C5+/+, C5−/− rats displayed significantly improved kidney injury/function as well as the attenuation of the apoptotic pathway post‐IRI. The circulating immune cell composition was affected by C5−/− post‐injury, with significantly increased NK cells, B cells, and CD8+ T‐cells compared to C5+/+, indicating altered inflammatory signaling. Similarly, renal sections showed a reduced level of immune cell infiltration, including macrophages and neutrophils. Collectively, these results demonstrate the generation of an effective rodent model of global C5 deletion and the role of C5 as an injury‐promoting molecule during kidney IRI.

Komivi Dossa, Mahugnon Ezékiel Houngbo, Jean-Luc Irep et al.

Abstract Background Yams (Dioscorea spp.) are significant food security crops especially in West Africa. With the increasing tuber demand and climate change challenges, it is pertinent to strengthen breeding programs for developing high-yielding cultivars with climate resilience. The current study aimed at deciphering the genetic basis of leaf traits related to stress responses in a diverse panel of Dioscorea alata genotypes. Results Phenotypic characterization of 12 traits, including leaf dry matter content, mean leaf area, net photosynthesis, transpiration rate, transpiration use efficiency, stomatal density, stomatal index, preformed node count, leaf thickness, competitor, stress-tolerator, ruderal ecological strategies emphasized significant variations among the genotypes and across two planting locations. Weak correlations were observed among most of traits, suggesting that breeding simultaneously for some of these stress response-related traits would be possible. Heritability was highest for transpiration rate, leaf area and stomatal density, while it was lowest for stress-tolerator, ruderal ecological strategies. Genome-wide association study (GWAS) using high-quality single nucleotide polymorphism (SNPs) identified 24 significant associations on 11 chromosomes, where the association signals were consistent across two locations for traits with high heritability, viz., stomatal density (Chr18) and transpiration rate (Chr3). Further characterization of the significant signals and their related alleles identified advantageous alleles contributing positively to the studied traits. Moreover, 44 putative candidate genes were identified. Dioal.18G049300 (3 keto acyl-coenzyme A synthase) was identified as a strong candidate gene for stomatal density, while Dioal.12G033600 (Phosphatidyl inositol monophosphate 5 kinase 4) was identified for net photosynthesis. Conclusion Taken together, GWAS and allele segregation analysis for key SNPs provided significant insights into the marker-trait associations, which can be further utilized in breeding programs to improve climate resilience in greater yam.

Ziyi Zhou, Ming Cheng, Yanjun Cui et al.

The increasing number of diabetic patients is a serious issue in society today, which has significant negative impacts on people's health and the country's financial expenditures. Because diabetes may develop into potential serious complications, early glucose prediction for diabetic patients is necessary for timely medical treatment. Existing glucose prediction methods typically utilize patients' private data (e.g. age, gender, ethnicity) and physiological parameters (e.g. blood pressure, heart rate) as reference features for glucose prediction, which inevitably leads to privacy protection concerns. Moreover, these models generally focus on either long-term (monthly-based) or short-term (minute-based) predictions. Long-term prediction methods are generally inaccurate because of the external uncertainties that can greatly affect the glucose values, while short-term ones fail to provide timely medical guidance. Based on the above issues, we propose CrossGP, a novel machine-learning framework for cross-day glucose prediction solely based on the patient's external activities without involving any physiological parameters. Meanwhile, we implement three baseline models for comparison. Extensive experiments on Anderson's dataset strongly demonstrate the superior performance of CrossGP and prove its potential for future real-life applications.

Sana Akhlaq, Saba Khaliq

The current study was planned to compare serum levels of secreted frizzled related protein-4, insulin resistance and waist-to-height ratio in individuals with and without a diabetic background, and to assess the correlation of these markers with family history of diabetes. The cross-sectional comparative study comprised 80 subjects with confirmed normal glucose tolerance values. Parameters assessed included secreted frizzled related protein-4, fasting glucose, random glucose, fasting insulin, homeostasis model of assessment of insulin resistance and waist-to-height ratio values. Those without a diabetic background had significantly higher frizzled related protein-4 levels (p=0.02). Although subjects with family history of diabetes showed higher mean fasting glucose, waist circumference and waist-to-height ratio, these differences were not statistically significant (p>0.05). However, there was a strong positive correlation with waist circumference, waist-to-height ratio, fasting insulin and homeostasis model of assessment of insulin resistance (p=0.0001). ---Continue

Matteo Raggi, Gennaro Boccia, Luca Mesin

Objective: Crosstalk in surface electromyogram (EMG) is an important open problem and the common strategy of reducing it through spatial filters needs improvements. Methods: We evaluated experimentally the optimal spatio-temporal filter (OSTF), i.e., a recent approach that adapts to the subject, filtering different EMG channels both in time and space to emphasize the signal of a target muscle discarding that of adjacent ones. EMGs were recorded by a high-density recording system from pronator teres (target muscle) and flexor carpi radialis (crosstalk muscle) of 8 healthy subjects. OSTF was tested in different conditions, considering one channel per muscle (either single or double differential, SD and DD, respectively), changing the selectivity of detection (small electrodes close to each other, or large ones with higher inter-electrode distance), the force applied by the muscles (whose EMGs were summed to simulate different levels of crosstalk), and the duration of the signal to train the method. Results: OSTF was less affected by crosstalk than SD and DD filters. Statistically significant improvements were obtained in reducing the crosstalk-induced variations: for example, considering small electrodes, we obtained a percentage error of 157.30±57.11 % and 38.54±10.47 % (mean±std) in the estimation of the average rectified value (ARV), and an error of 23.57±3.92 % and 8.31±0.88 % in the estimation of the median frequency (MDF), for SD and OSTF, respectively. Conclusion: The OSTF can be applied in real-time, is easy to use, and is feasible even when using only few detection channels, as is customary in many applications.

Kirti Singhal, Surekha Chukkali

AbstractDespite the advances in studies conducted among healthcare professionals to explore the impact of the pandemic on their mental health, a large population still continues to display COVID-19 related psychological complaints. There has been recent awareness of moral injury related guilt and shame among doctors and nurses. However, the factors associated with moral injury have not received much attention, due to which the issue still persists. This study aims to explore the role of guilt-shame proneness, and locus of control in predicting moral injury among healthcare professionals. MISS-HP, PGI Locus of Control, and GASP scales were administered to a sample of 806 healthcare professionals. Pearson correlation coefficient indicated a significant positive relationship between moral injury and guilt-shame proneness, as well as the locus of control. Regression analysis indicated a significant role of guilt-shame proneness and locus of control in predicting moral injury. In conclusion, while studying moral injury, it becomes equally important to consider these factors to understand the concept better.

Michela Balconi, Michela Balconi, Roberta A. Allegretta et al.

According to previous research, people influence each other’s emotional states during social interactions via resonance mechanisms and coordinated autonomic rhythms. However, no previous studies tested if the manipulation of the interoceptive focus (focused attention on the breath for a given time interval) in hyperscanning during synchronized tasks may have an impact on autonomic synchrony. Thus, this study aims to assess the psychophysiological synchrony through autonomic measures recording during dyadic linguistic and motor synchronization tasks performed in two distinct interoceptive conditions: the focus and no focus on the breath condition. 26 participants coupled in 13 dyads were recruited. Individuals’ autonomic measures [electrodermal: skin conductance level and response (SCL, SCR); cardiovascular indices: heart rate (HR) and HR variability (HRV)] was continuously monitored during the experiment and correlational coefficients were computed to analyze dyads physiological synchrony. Inter-subject analysis revealed higher synchrony for HR, HRV, SCL, and SCR values in the focus compared to no focus condition during the motor synchronization task and in general more for motor than linguistic task. Higher synchrony was also found for HR, SCL, and SCR values during focus than no focus condition in linguistic task. Overall, evidence suggests that the manipulation of the interoceptive focus has an impact on the autonomic synchrony during distinct synchronization tasks and for different autonomic measures. Such findings encourage the use of hyperscanning paradigms to assess the effect of breath awareness practices on autonomic synchrony in ecological and real-time conditions involving synchronization.

Maryam Khalid, Emily Willis

Emotion prediction is a key emerging research area that focuses on identifying and forecasting the emotional state of a human from multiple modalities. Among other data sources, physiological data can serve as an indicator for emotions with an added advantage that it cannot be masked/tampered by the individual and can be easily collected. This paper surveys multiple machine learning methods that deploy smartphone and physiological data to predict emotions in real-time, using self-reported ecological momentary assessments (EMA) scores as ground-truth. Comparing regression, long short-term memory (LSTM) networks, convolutional neural networks (CNN), reinforcement online learning (ROL), and deep belief networks (DBN), we showcase the variability of machine learning methods employed to achieve accurate emotion prediction. We compare the state-of-the-art methods and highlight that experimental performance is still not very good. The performance can be improved in future works by considering the following issues: improving scalability and generalizability, synchronizing multimodal data, optimizing EMA sampling, integrating adaptability with sequence prediction, collecting unbiased data, and leveraging sophisticated feature engineering techniques.

Christopher Cox, Michael W. Plesniak

We numerically investigate the influence of flow development on secondary flow patterns and subsequent wall shear stress distributions in a curved artery model, and we compute vascular metrics commonly used to assess variations in blood flow characteristics as it applies to arterial disease. We model a human artery with a simple, rigid 180-degree curved tube with circular cross-section and constant curvature, neglecting effects of taper, torsion and elasticity. High-fidelity numerical results are computed from an in-house discontinuous spectral element flow solver. The flow rate used in this study is physiological. We perform this study using a Newtonian blood-analog fluid subjected to a pulsatile flow with two inflow conditions. The first flow condition is fully developed while the second condition is undeveloped (i.e. uniform). We observe and discuss differences in secondary flow patterns that emerge over the rapid acceleration and deceleration phases of the physiological waveform, and we directly connect the variation in intensity of these secondary flow patterns along the curvature to differences in the wall shear stress metrics for each entrance condition. Results indicate that decreased axial velocities under an undeveloped condition produce less intense secondary flow that, in turn, reduces both the oscillatory and multidirectional nature of the wall shear stress vector, and we link this effect to abnormalities in computed stress metrics. These results suggest potentially lower prevalence of disease in curvatures where entrance flow is rather undeveloped-a physiologically relevant result to further understand the influence of blood flow development on disease.

Alexandre Duval, Anita Paas, Abdalwhab Abdalwhab et al.

The drone industry is diversifying and the number of pilots increases rapidly. In this context, flight schools need adapted tools to train pilots, most importantly with regard to their own awareness of their physiological and cognitive limits. In civil and military aviation, pilots can train themselves on realistic simulators to tune their reaction and reflexes, but also to gather data on their piloting behavior and physiological states. It helps them to improve their performances. Opposed to cockpit scenarios, drone teleoperation is conducted outdoor in the field, thus with only limited potential from desktop simulation training. This work aims to provide a solution to gather pilots behavior out in the field and help them increase their performance. We combined advance object detection from a frontal camera to gaze and heart-rate variability measurements. We observed pilots and analyze their behavior over three flight challenges. We believe this tool can support pilots both in their training and in their regular flight tasks. A demonstration video is available on https://www.youtube.com/watch?v=eePhjd2qNiI

James O Sullivan, Mohammad Arif Ul Alam

Person re-identification is a critical privacy breach in publicly shared healthcare data. We investigate the possibility of a new type of privacy threat on publicly shared privacy insensitive large scale wearable sensing data. In this paper, we investigate user specific biometric signatures in terms of two contextual biometric traits, physiological (photoplethysmography and electrodermal activity) and physical (accelerometer) contexts. In this regard, we propose PhysioGait, a context-aware physiological signal model that consists of a Multi-Modal Siamese Convolutional Neural Network (mmSNN) which learns the spatial and temporal information individually and performs sensor fusion in a Siamese cost with the objective of predicting a person's identity. We evaluated PhysioGait attack model using 4 real-time collected datasets (3-data under IRB #HP-00064387 and one publicly available data) and two combined datasets achieving 89% - 93% accuracy of re-identifying persons.

Koji Hosokawa, Katsuya Tanaka, Kayo Ishihara et al.

Abstract Purpose Early discontinuation of postoperative oxygen support (POS) would partially depend on the innate pulmonary physics. We aimed to examine if the initial driving pressure (dP) at the induction of general anesthesia (GA) predicted POS prolongation. Methods We conducted a single-center retrospective study using the facility's database. Consecutive subjects over 2 years were studied to determine the change in odds ratio (OR) for POS prolongation of different dP classes at GA induction. The dP (cmH2O) was calculated as the ratio of tidal volume (mL) over dynamic Crs (mL/cmH2O) regardless of the respiratory mode. The adjusted OR was calculated using the logistic regression model of multivariate analysis. Moreover, we performed a secondary subgroup analysis of age and the duration of GA. Results We included 5,607 miscellaneous subjects. Old age, high scores of American Society of Anesthesiologist physical status, initial dP, and long GA duration were associated with prolonged POS. The dP at the induction of GA (7.78 [6.48, 9.45] in median [interquartile range]) was categorized into five classes. With the dP group of 6.5–8.3 cmH2O as the reference, high dPs of 10.3–13 cmH2O and ≥ 13 cmH2O were associated with significant prolongation of POS (adjusted OR, 1.62 [1.19, 2.20], p = 0.002 and 1.92 [1.20, 3.05], p = 0.006, respectively). The subgroup analysis revealed that the OR for prolonged POS of high dPs disappeared in the aged and ≥ 6 h anesthesia time subgroup. Conclusions High initial dPs ≥ 10 cmH2O at GA induction predicted longer POS than those of approximately 7 cmH2O. High initial dPs were, however, a secondary factor for prolongation of postoperative hypoxemia in old age and prolonged surgery.

Huasheng CAO, Fujun WANG, Shuguang LI et al.

Guang 8A is a wild abortion Simiao type male sterile line of indica rice with high quality and high yield, which was developed by crossing the female parent Zengcheng Simiao-8 (a high-quality indica rice possessing relationship with Guangdong wild rice) with the maintainer line 1325B, and then backcrossing with 325A after test-crossing. It was selected by the Rice Research Institute of Guangdong Academy of Agricultural Sciences, with the characteristics of good quality, strong combining ability, disease resistance and excellent comprehensive agronomic characters. Since it passed the technical identification of Guangdong Province in 2010, 44 new rice varieties have been approved or authorized by the state and provincial governments in China, including Guangdong, Guangxi, Fujian, Yunnan and Sichuan Province (Region). All of these varieties showed the characteristics of high quality, high and stable yield and strong adaptability, fully demonstrating the role of Guang 8A as the "Core Rice of Guangdong". In addition, it was selected as test material and widely applied in the basic theory researches of cultivation and physiology. Among them, Guang 8 you 165, Guang 8 you Jinzhan and Guang 8 you 2168 showed high coordination of abundance, resistance and quality in provincial regional tests and have been selected as the leading agricultural varieties in Guangdong Province for four consecutive years. From 2017 to date, the accumulative promotion area in Guangdong has reached more than 0.26 million hm2, which has extremely important production value in South China and the middle and lower reaches of the Yangtze River.