AIMC Topic: Heart Rate
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Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices.
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 3, 2024
BackgroundAnxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tracking emotional states frequently lack the accuracy and sensitivity needed for successful intervent...
Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques.
Journal of sports science & medicine
Dec 1, 2024
This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a ...
Left Atrial Wall Thickness Measured by a Machine Learning Method Predicts AF Recurrence After Pulmonary Vein Isolation.
Journal of cardiovascular electrophysiology
Nov 29, 2024
BACKGROUND: Left atrial (LA) remodeling plays a significant role in the progression of atrial fibrillation (AF). Although LA wall thickness (LAWT) has emerged as an indicator of structural remodeling, its impact on AF outcomes remains unclear. We aim...
Atrial Remodeling
Risk Assessment
Humans
Time Factors
Heart Rate
Risk Factors
Treatment Outcome
Machine Learning
Catheter Ablation
Registries
Aged
Retrospective Studies
Recurrence
Action Potentials
Fibrosis
Male
Female
Atrial Function, Left
Atrial Fibrillation
Predictive Value of Tests
Middle Aged
Pulmonary Veins
Heart Atria
Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.
Sensors (Basel, Switzerland)
Nov 26, 2024
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light inten...
Assessing operator stress in collaborative robotics: A multimodal approach.
Applied ergonomics
Nov 16, 2024
In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator approaching a collaborative robot (cobot) may have feelings of distrust, and experience discomfort and...
A non-invasive heart rate prediction method using a convolutional approach.
Medical & biological engineering & computing
Nov 15, 2024
The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the glob...
Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.
Circulation. Cardiovascular quality and outcomes
Nov 14, 2024
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. ...
A fuzzy-logic approach for longitudinal assessment of patients' psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation.
Journal of neuroengineering and rehabilitation
Nov 8, 2024
Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to as...
Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.
Circulation. Arrhythmia and electrophysiology
Oct 24, 2024
BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertab...
Electrocardiogram and respiration recordings show a reduction in the physical burden on professional caregivers when performing care tasks with a transfer support robot.
Assistive technology : the official journal of RESNA
Oct 21, 2024
In this study, we assessed the physical burden on professional caregivers when using a transfer support robot, "Hug," to transfer and move a care recipient. We compared heart rate (HR), heart rate variability (HRV), and the time-series synchronizatio...
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