AIMC Topic: Heart Rate
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Machine learning evaluation model of pilot workload in a low-visibility environment.
Scientific reports
Jul 1, 2025
To analyze the variation trend of pilots' workload in a low-visibility flight environment and then put forward a scientific evaluation method, this study set up an experimental platform using an E01-pro simulated flight platform and a PhysioPlux mult...
Evaluating a brief smartphone-based stress management intervention with heart rate biofeedback from built-in sensors in a three arm randomized controlled trial.
Scientific reports
Jun 23, 2025
Perceived stress is prevalent in industrial societies, negatively impacting mental health. Smartphone-based stress management interventions provide accessible alternatives to traditional methods, but their efficacy remains modest, potentially due to ...
A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.
Proceedings of the National Academy of Sciences of the United States of America
Jun 6, 2025
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...
Emotion recognition with multiple physiological parameters based on ensemble learning.
Scientific reports
Jun 6, 2025
Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing user experience design. This study explores the application and effectiveness of ensemble learning m...
Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol.
BMJ open
May 24, 2025
INTRODUCTION: Physical activity (PA) is crucial for older adults' well-being and mitigating health risks. Encouraging active lifestyles requires a deeper understanding of the factors influencing PA, which conventional approaches often overlook by ass...
Cyberdelics: Virtual reality hallucinations modulate cognitive-affective processes.
Dialogues in clinical neuroscience
May 23, 2025
INTRODUCTION: Psychedelics were explored for their potential in the mental health field. However, research was delayed by concerns over short-term side effects and long-term consequences of substance use. Technological advances enabled the developmen...
Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training.
Sensors (Basel, Switzerland)
May 20, 2025
This study explores nursing students' stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal act...
Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.
Nature cardiovascular research
May 16, 2025
Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncoverin...
Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation.
The international journal of cardiovascular imaging
Apr 29, 2025
Real time 2D phase contrast (RTPC) MRI is useful for flow quantification in atrial fibrillation (AF) patients, but data analysis requires time-consuming anatomical contouring for many cardiac time frames. Our goal was to develop a convolutional neura...
Middle Aged
Deep Learning
Time Factors
Magnetic Resonance Imaging
Female
Reproducibility of Results
Aged
Male
Image Interpretation, Computer-Assisted
Observer Variation
Automation
Blood Flow Velocity
Atrial Function, Left
Predictive Value of Tests
Atrial Fibrillation
Heart Atria
Heart Rate
Humans
Magnetic Resonance Imaging, Cine
Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.
BMC cardiovascular disorders
Apr 28, 2025
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...
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