Exercise & Fitness

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

The educational environment plays a vital role in the development of students who participate in ath...

Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.

PURPOSE: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardi...

Predicting long-term sleep deprivation using wearable sensors and health surveys.

Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have sig...

Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.

(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) ...

Temporal Convolutional Neural Network-Based Prediction of Vascular Health in Elderly Women Using Photoplethysmography-Derived Pulse Wave during Exercise.

(1) Background: The objective of this study was to predict the vascular health status of elderly wom...

Online soft measurement method for chemical oxygen demand based on CNN-BiLSTM-Attention algorithm.

The measurement of chemical oxygen demand (COD) is very important in the process of sewage treatment...

Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.

Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes melli...

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high r...

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders.

Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce produ...

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prev...

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management ...

Clinicians and AI use: where is the professional guidance?

With the introduction of artificial intelligence (AI) to healthcare, there is also a need for profes...

Engaging nurses in developing generative artificial intelligence-based technologies can enhance their work motivation, engagement and satisfaction.

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased h...

Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsiv...

Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms.

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related ...

CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, ...

Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment.

Human pose estimation (HPE) is a technique used in computer vision and artificial intelligence to de...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients w...

Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dy...

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an impo...