Hasil untuk "Physiology"

Yufei Cao, Penny Sweetser, Ziyu Chen et al.

User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identification of users struggling with task demands. While ocular and cardiac signals are widely used to characterise performance-relevant visual behaviour and physiological activation, their potential for early prediction and for revealing the physiological mechanisms underlying performance differences remains underexplored. We conducted a within-subject experiment in a game environment with naturally unfolding complexity, using early ocular and cardiac signals to predict later performance and to examine physiological and self-reported group differences. Results show that the ocular-cardiac fusion model achieves a balanced accuracy of 0.86, and the ocular-only model shows comparable predictive power. High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified, with a more positive affective experience. These findings demonstrate the feasibility of cross-session prediction from early physiology, providing interpretable insights into performance variation and facilitating future proactive intervention.

Rihao Chang, He Jiao, Weizhi Nie et al.

Recent advances in large language models (LLMs) have enabled new possibilities in simulating complex physiological systems. We introduce Organ-Agents, a multi-agent framework that simulates human physiology via LLM-driven agents. Each Simulator models a specific system (e.g., cardiovascular, renal, immune). Training consists of supervised fine-tuning on system-specific time-series data, followed by reinforcement-guided coordination using dynamic reference selection and error correction. We curated data from 7,134 sepsis patients and 7,895 controls, generating high-resolution trajectories across 9 systems and 125 variables. Organ-Agents achieved high simulation accuracy on 4,509 held-out patients, with per-system MSEs <0.16 and robustness across SOFA-based severity strata. External validation on 22,689 ICU patients from two hospitals showed moderate degradation under distribution shifts with stable simulation. Organ-Agents faithfully reproduces critical multi-system events (e.g., hypotension, hyperlactatemia, hypoxemia) with coherent timing and phase progression. Evaluation by 15 critical care physicians confirmed realism and physiological plausibility (mean Likert ratings 3.9 and 3.7). Organ-Agents also enables counterfactual simulations under alternative sepsis treatment strategies, generating trajectories and APACHE II scores aligned with matched real-world patients. In downstream early warning tasks, classifiers trained on synthetic data showed minimal AUROC drops (<0.04), indicating preserved decision-relevant patterns. These results position Organ-Agents as a credible, interpretable, and generalizable digital twin for precision diagnosis, treatment simulation, and hypothesis testing in critical care.

Kleanthis Avramidis, Emily Zhou, Tiantian Feng et al.

Understanding and mitigating driving stress is vital for preventing accidents and advancing both road safety and driver well-being. While vehicles are equipped with increasingly sophisticated safety systems, many limits exist in their ability to account for variable driving behaviors and environmental contexts. In this study we examine how short-term stressor events impact drivers' physiology and their behavioral responses behind the wheel. Leveraging a controlled driving simulation setup, we collected physiological signals from 31 adult participants and designed a multimodal machine learning system to estimate the presence of stressors. Our analysis explores the model sensitivity and temporal dynamics against both known and novel emotional inducers, and examines the relationship between predicted stress and observable patterns of vehicle control. Overall, this study demonstrates the potential of linking physiological signals with contextual and behavioral cues in order to improve real-time estimation of driving stress.

Chenyu Tang, Yu Zhu, Josée Mallah et al.

Wearable exoskeletons hold transformative promise for restoring mobility across diverse users with muscular weakness or other impairments. However, their translation beyond laboratory environments remains limited by sensing systems that capture movement but not underlying physiology. Here, we present a soft, lightweight smart leg sleeve that achieves anatomically aligned, layered multimodal sensing by integrating textile-based surface electromyography (sEMG) electrodes, ultrasensitive textile strain sensors, and inertial measurement units (IMUs). Each sensing modality targets a distinct physiological layer: IMUs track joint kinematics at the skeletal level, sEMG monitors muscle activation at the muscular level, and strain sensors detect skin deformation at the cutaneous level. Together, these sensors provide real-time perception to support three core objectives: controlling personalized assistance, optimizing user effort, and safeguarding against injury risks. The system is skin-conformal, mechanically compliant, and seamlessly integrated with a custom exoskeleton ($<20$~g total sensor and electronics weight). We demonstrate: (1) accurate ankle joint moment estimation (RMSE = 0.13~Nm/kg), (2) real-time classification of metabolic trends (accuracy = 97.1\%), and (3) injury risk detection within 100~ms (recall = 0.96), all validated on unseen users using a leave-one-subject-out protocol. This work establishes a physiology-aligned sensing architecture that reframes exoskeleton perception from motion tracking to real-time physiological decoding, offering a pathway towards intelligent, adaptive, and personalized wearable robotics.

Vishwanath Pratap Singh, Md. Sahidullah, Tomi Kinnunen

The increasing use of children's automatic speech recognition (ASR) systems has spurred research efforts to improve the accuracy of models designed for children's speech in recent years. The current approach utilizes either open-source speech foundation models (SFMs) directly or fine-tuning them with children's speech data. These SFMs, whether open-source or fine-tuned for children, often exhibit higher word error rates (WERs) compared to adult speech. However, there is a lack of systemic analysis of the cause of this degraded performance of SFMs. Understanding and addressing the reasons behind this performance disparity is crucial for improving the accuracy of SFMs for children's speech. Our study addresses this gap by investigating the causes of accuracy degradation and the primary contributors to WER in children's speech. In the first part of the study, we conduct a comprehensive benchmarking study on two self-supervised SFMs (Wav2Vec2.0 and Hubert) and two weakly supervised SFMs (Whisper and MMS) across various age groups on two children speech corpora, establishing the raw data for the causal inference analysis in the second part. In the second part of the study, we analyze the impact of physiological factors (age, gender), cognitive factors (pronunciation ability), and external factors (vocabulary difficulty, background noise, and word count) on SFM accuracy in children's speech using causal inference. The results indicate that physiology (age) and particular external factor (number of words in audio) have the highest impact on accuracy, followed by background noise and pronunciation ability. Fine-tuning SFMs on children's speech reduces sensitivity to physiological and cognitive factors, while sensitivity to the number of words in audio persists. Keywords: Children's ASR, Speech Foundational Models, Causal Inference, Physiology, Cognition, Pronunciation

Jiankai Tang, Kegang Wang, Yingke Ding et al.

Smart rings offer a convenient way to continuously and unobtrusively monitor cardiovascular physiological signals. However, a gap remains between the ring hardware and reliable methods for estimating cardiovascular parameters, partly due to the lack of publicly available datasets and standardized analysis tools. In this work, we present $τ$-Ring, the first open-source ring-based dataset designed for cardiovascular physiological sensing. The dataset comprises photoplethysmography signals (infrared and red channels) and 3-axis accelerometer data collected from two rings (reflective and transmissive optical paths), with 28.21 hours of raw data from 34 subjects across seven activities. $τ$-Ring encompasses both stationary and motion scenarios, as well as stimulus-evoked abnormal physiological states, annotated with four ground-truth labels: heart rate, respiratory rate, oxygen saturation, and blood pressure. Using our proposed RingTool toolkit, we evaluated three widely-used physics-based methods and four cutting-edge deep learning approaches. Our results show superior performance compared to commercial rings, achieving best MAE values of 5.18 BPM for heart rate, 2.98 BPM for respiratory rate, 3.22\% for oxygen saturation, and 13.33/7.56 mmHg for systolic/diastolic blood pressure estimation. The open-sourced dataset and toolkit aim to foster further research and community-driven advances in ring-based cardiovascular health sensing.

Weitao Tang, Johann Vargas-Calixto, Nasim Katebi et al.

Study Objectives: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first review to integrate more than seven decades of research into a unified, cross-species synthesis of fetal sleep. We examine: (i) Physiology and Ontogeny-comparing human fetuses with animal models; and (ii) Methodological Evolution-transitioning from invasive neurophysiology to non-invasive monitoring and deep learning frameworks. Methods: A structured narrative synthesis was guided by a systematic literature search across four databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). From 2,925 identified records, 171 studies involving fetal sleep-related physiology, sleep-state classification, or signal-based monitoring were included in this review. Results: Across the 171 studies, fetal sleep states become clearly observable as the brain matures. In fetal sheep and baboons, organized cycling between active and quiet sleep emerges at approximately 80%-90% gestation. In humans, this differentiation occurs later, around 95% gestation, with full maturation reached near term. Despite extensive animal research, no unified, clinically validated framework exists for defining fetal sleep states, limiting translation into routine obstetric practice. Conclusions: By integrating evidence across species, methodologies, and clinical contexts, this review provides the scientific foundation for developing objective, multimodal, and non-invasive fetal sleep monitoring technologies-tools that may ultimately support earlier detection of neurological compromise and guide timely prenatal intervention.

Marzena Zielińska, Alicja Bartkowska-Śniatkowska, Magdalena Mierzewska-Schmidt et al.

The anaesthesia of a young child under 3 years of age is a challenge for every anaesthetist. The peculiarities of this group of patients, particularly neonates and infants, resulting primarily from differences in both physiology, anatomy and the immaturity of individual organs which translate into different pharmacokinetics and pharmacodynamics of the drugs used in anaesthesiology, underlie the significantly more frequently recorded critical events during anaesthesia compared with the adult patient population. Concerned about the safety of children undergoing anaesthesia and aiming to ensure the highest possible quality and uniform standard of anaesthetic services, the Expert Panel of the Section of Paediatric Anaesthesiology and Intensive Care has prepared a Section position paper on anaesthesia in children under 3 years of age.

Laura Hjort Blicher, Peter Emil Carstensen, Jacob Bendsen et al.

Physiological whole-body models are valuable tools for the development of novel drugs where understanding the system aspects is important. This paper presents a generalized model that encapsulates the structure and flow of whole-body human physiology. The model contains vascular, interstitial, and cellular subcompartments for each organ. Scaling of volumes and blood flows is described to allow for investigation across populations or specific patient groups. The model equations and the corresponding parameters are presented along with a catalog of functions that can be used to define the organ transport model and the biochemical reaction model. A simple example illustrates the procedure.

David Gamez, Dionis Barcari, Aliya Grig

Foundation models have had a big impact in recent years and billions of dollars are being invested in them in the current AI boom. The more popular ones, such as Chat-GPT, are trained on large amounts of data from the Internet, and then reinforcement learning, RAG, prompt engineering and cognitive modelling are used to fine-tune and augment their behavior. This technology has been used to create models of individual people, such as Caryn Marjorie. However, these chatbots are not based on people's actual emotional and physiological responses to their environment, so they are, at best, surface-level approximations to the characters they are imitating. This paper describes how a new type of foundation model - a first-person foundation model - could be created from recordings of what a person sees and hears as well as their emotional and physiological reactions to these stimuli. A first-person foundation model would map environmental stimuli to a person's emotional and physiological states, and map a person's emotional and physiological states to their behavior. First-person foundation models have many exciting applications, including a new type of recommendation engine, personal assistants, generative adversarial networks, dating and recruitment. To obtain training data for a first-person foundation model, we have developed a recording rig that captures what the wearer is seeing and hearing as well as their emotional and physiological states. This novel source of data could help to address the shortage of new data for building the next generation of foundation models.

Jiyao Wang, Hao Lu, Ange Wang et al.

Remote photoplethysmography (rPPG) has been widely applied to measure heart rate from face videos. To increase the generalizability of the algorithms, domain generalization (DG) attracted increasing attention in rPPG. However, when rPPG is extended to simultaneously measure more vital signs (e.g., respiration and blood oxygen saturation), achieving generalizability brings new challenges. Although partial features shared among different physiological signals can benefit multi-task learning, the sparse and imbalanced target label space brings the seesaw effect over task-specific feature learning. To resolve this problem, we designed an end-to-end Mixture of Low-rank Experts for multi-task remote Physiological measurement (PhysMLE), which is based on multiple low-rank experts with a novel router mechanism, thereby enabling the model to adeptly handle both specifications and correlations within tasks. Additionally, we introduced prior knowledge from physiology among tasks to overcome the imbalance of label space under real-world multi-task physiological measurement. For fair and comprehensive evaluations, this paper proposed a large-scale multi-task generalization benchmark, named Multi-Source Synsemantic Domain Generalization (MSSDG) protocol. Extensive experiments with MSSDG and intra-dataset have shown the effectiveness and efficiency of PhysMLE. In addition, a new dataset was collected and made publicly available to meet the needs of the MSSDG.

Keith Davis, Tuukka Ruotsalo

Wearable devices that measure and record physiological signals are now becoming widely available to the general public with ever-increasing affordability and signal quality. The data from these devices introduce serious ethical challenges that remain largely unaddressed. Users do not always understand how these data can be leveraged to reveal private information about them and developers of these devices may not fully grasp how physiological data collected today could be used in the future for completely different purposes. We discuss the potential for wearable devices, initially designed to help users improve their well-being or enhance the experience of some digital application, to be appropriated in ways that extend far beyond their original intended purpose. We identify how the currently available technology can be misused, discuss how pairing physiological data with non-physiological data can radically expand the predictive capacity of physiological wearables, and explore the implications of these expanded capacities for a variety of stakeholders.

Chizoba M. Enemchukwu, Christiana Nwabueze, Oluchi J. Osuala et al.

Background: The adult human vagina hosts a complex biota containing diverse communities of microorganisms. The occurrence of multi-drug-resistant strains of these microorganisms has persistently increased due to poor hygiene and misuse or abuse of antibiotics. The vaginal microflora may exhibit patterns of growth, biochemical expression, or response to the standard drugs which consequently lead to answer the complex questions of antimicrobial resistance. Aim: The study aimed to quantify the susceptibility profile of microorganisms isolated from vaginal discharge and evaluate the minimum inhibitory concentration of diverse antimicrobial drugs. Methods: Fifty vaginal swabs were collected from female students of Madonna University, Nigeria while two samples were collected each from a pregnant and a non-pregnant woman at the university’s tertiary care teaching hospital. The isolates were grown in selective media and identified through Gram-staining and biochemical physiology for identification. The Kirby-Bauer disc diffusion method was used for microbial susceptibility testing, and the agar dilution method was used to determine the minimum inhibitory concentration of commonly prescribed antibiotics at the teaching hospital. Results: Sixty-eight microorganisms comprising 17 Gram-positive (Staphylococcus sp.) and 31 Gram-negative (Escherichia coli and others) bacteria and 20 fungi (Candida sp.) were isolated. The bacteria showed a high resistance (>80%) to amoxicillin, cefuroxime, and cefixime but were relatively susceptible (35–100%) to levofloxacin and ofloxacin. Cefepime showed high activity with a minimum inhibitory concentration range of 25–50 µg/mL against the studied bacteria. The isolated fungi were susceptible to amphotericin B (35–40%) but resistant (>85%) to other antifungal drugs tested. Conclusion: The study suggests that bacterial vaginosis prevalence at the university could best be treated with ofloxacin (second generation- fluoroquinlone), levofloxacin (third generation- fluoroquinolone), and cefepime (fourth generation- cephalosporin) due to their greater sensitivity, while candidiasis could best be treated with amphotericin B (a pyolene).

Daniella Tosta Link, Gustavo Guimarães Fernandes Viana, Lívia Pasolini Siqueira et al.

The consumption of seafood is crucial for food security, but poor hygiene along the food production chain can result in low microbiological quality, posing significant risks for public health and seafood quality. Thus, this study aimed to assess the microbiological quality and antimicrobial sensitivity of <i>E. coli</i> from 69 samples of illegally marketed shrimp and mussels in the Vitória Region, Brazil. These foods exhibited poor microbiological quality due to high counts of mesophilic, psychrotrophic, and enterobacteria microorganisms. While this issue is widespread in this area, shrimp samples displayed higher microbial counts compared to mussels, and fresh mussels had elevated counts of enterobacteria compared to frozen ones. Among the 10 <i>E. coli</i> isolates, none carried the genes <i>blaCTX-M-1</i>, <i>blaCTX-M-2</i>, <i>blaCTX-M-3</i>, <i>blaCTX-M-15</i>, <i>mcr-1</i>, <i>mcr-2</i>, <i>mcr-3</i>, <i>mcr-4</i>, and <i>tet</i>, associated with antibiotic resistance. Phenotypical resistance to tetracycline and fosfomycin was not observed in any isolate, while only 20% demonstrated resistance to ciprofloxacin. Regarding ampicillin and amoxicillin with clavulanic acid, 60% of isolates were resistant, 10% showed intermediate susceptibility, and 30% were sensitive. One isolate was considered simultaneously resistant to β-lactams and quinolones, and none were conserved as ESBL producers. These findings highlight the inherent risks to local public health that arise from consuming improperly prepared seafood in this area.

Abebe Bereda, Beshah Agune, Zelalem Yilma

The objectives of this research were to assess the productive and reproductive performances, and crossbred Jersey farmers preferences traits in Angolelana Tera district, Amhara region, Ethiopia. A total of 158 smallholders holding Jersey crossbreed recruited from seven Kebeles were interviewed face to face using a semi structured questionnaire. Index for traits preferences and one-way analysis of variance for reproductive and productive performances differences of dairy cattle breeds were employed using Statistical Package for Social Science (SPSS) version 25. Milk yield, growth and traction in Holstein-Friesian crossbred, while milk fat, fertility and growth in Jersey crossbred, and disease’s resistant ability, traction power and adaptation to local climates in indigenous breed were the most valued traits by farmers in the study district. The overall mean of the most reproductive and productive performance parameters of Jersey crossbred namely age at first calving (30.56 months), lactation length (241.32 days), calving interval (441.79 days), number of services per conception (1.55) and longevity (16.53 years) were significantly better than its counterpart Holstein-Friesian crossbreed with the corresponding values of 32.91 months, 227.28 days, 447.59 days, 1.86 and 15.03 years, respectively. However, no significant variations were observed between the two crossbreeds of Holstein-Friesian and Jersey on age at first service and days open in the present study. Generally, indigenous breeds significantly lower productive and reproductive performances than crossbred cattle. In conclusion, although some parameters are antagonistic with the standards recommended for modern dairy farming, Jersey crossbreed performed well in the district. These indicates that the project looks targeted on the right breed in the right place with necessary training approaches on caring of crossbred Jersey breed and milk processing. Therefore, the wide use of Jersey crossbreed must be promoted in the district together with necessary improved management packages.

Xu Rongchang

lumbodorsal myofasciitis is a common injury in badminton and a common disease in clinical practice. Clinically, acupuncture, cupping, massage, extracorporeal shock wave and other methods are usually used. This paper summarizes and classifies the commonly used treatment methods in the clinic, which are mainly divided into traditional Chinese medicine (TCM) combined treatment methods, Chinese medicine treatment combined physical therapy methods and physical combined therapy. In the clinic, TCM combined treatment methods and TCM combined physical therapy methods are mainly used, and TCM combined physical therapy methods should be the main research direction in the future. There are many therapeutic methods, but the efficacy of combining multiple therapeutic methods is better than that of single method. This study can provide a theoretical basis and reference for subsequent research and clinical treatment.

David Berga, Alexandre Pereda, Eleonora De Filippi et al.

Esports gaming is an area in which videogame players need to cooperate and compete with each other, influencing their cognitive load, processing, stress, and social skills. Here it is unknown to which extent competitive videogame play using a desktop setting can affect the physiological responses of players' autonomic nervous system. For such, we propose a study where we have measured distinct electrodermal and cardiac activity metrics over competitive players during several League of Legends gameplay sessions in a Esports stadium. We mainly found that game performance (whether winning or losing the game) significantly affects both electrodermal and cardiac activity, where players who lost the game showed higher stress-related physiological responses, as compared to winning players. We also found that important specific in-game events such as "Killing", "Dying" or "Destroying Turret" significantly increased both electrodermal and cardiac activity over players more than other less-relevant events such as "Placing Wards" or "Destroying Turret Plates". Finally, by analyzing activity over player roles we found different trends of activity on these measurements, this could foster the exploration on human physiology with a higher set of participants in future Esports studies.

Luis F. Gomez, Julian Fierrez, Aythami Morales et al.

Presentation Attack Detection (PAD) is a crucial stage in facial recognition systems to avoid leakage of personal information or spoofing of identity to entities. Recently, pulse detection based on remote photoplethysmography (rPPG) has been shown to be effective in face presentation attack detection. This work presents three different approaches to the presentation attack detection based on rPPG: (i) The physiological domain, a domain using rPPG-based models, (ii) the Deepfakes domain, a domain where models were retrained from the physiological domain to specific Deepfakes detection tasks; and (iii) a new Presentation Attack domain was trained by applying transfer learning from the two previous domains to improve the capability to differentiate between bona-fides and attacks. The results show the efficiency of the rPPG-based models for presentation attack detection, evidencing a 21.70% decrease in average classification error rate (ACER) (from 41.03% to 19.32%) when the presentation attack domain is compared to the physiological and Deepfakes domains. Our experiments highlight the efficiency of transfer learning in rPPG-based models and perform well in presentation attack detection in instruments that do not allow copying of this physiological feature.

Francesca De Benetti, Walter Simson, Magdalini Paschali et al.

Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes. Voxel-wise physiologically-based pharmacokinetic (PBPK) modeling of the time activity curves (TAC) can provide relevant diagnostic information for clinical workflow. Conventional fitting strategies for TACs are slow and ignore the spatial relation between neighboring voxels. We train a spatio-temporal UNet to estimate the kinetic parameters given TAC from F-18-fluorodeoxyglucose (FDG) dPET. This work introduces a self-supervised loss formulation to enforce the similarity between the measured TAC and those generated with the learned kinetic parameters. Our method provides quantitatively comparable results at organ-level to the significantly slower conventional approaches, while generating pixel-wise parametric images which are consistent with expected physiology. To the best of our knowledge, this is the first self-supervised network that allows voxel-wise computation of kinetic parameters consistent with a non-linear kinetic model. The code will become publicly available upon acceptance.

Gennadi Saiko

With the proliferation of inexpensive CMOS cameras, medical imaging experiences a noticeable influx of new technologies. While anatomical imaging is based on well-established principles of photography, physiological optical imaging is a relatively novel range of technologies that requires considering a new set of technical and physiological aspects. We discuss several factors (binning, spatial frequency sampling, and distance to the target area) indispensable to getting quantifiable and reproducible results, which are the essence of physiological imaging. We also discussed their implications for several commonly used physiological imaging modalities, including hyperspectral/multispectral imaging, fluorescence imaging, and thermography. Physiological imaging technologies are in their infancy. Thus, the proper design, which considers technical and physiological aspects, is paramount to establishing their credibility and driving clinical adoption.