Latest AI and machine learning research in exercise & fitness for healthcare professionals.
Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined health monitoring tasks, including activity recognition, fitness tracking, and cardiovascular signal assessment. However, most existing WFMs primarily map short temporal windows to predefined labels via static encoders, emp...
Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) success using machine learning, and to integrate neonatal morbidity outcomes into a decision-analytic framework for trial of labor after cesarean (TOLAC) counseling. Methods: We performed a retrospective cohort study of 1,418 consecutive TOLAC cases at a ...
Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and...
Stroke is a leading cause of mortality and morbidity worldwide. MRI-visible perivascular spaces (PVS) are an emerging marker of cerebral small vessel ...
Objectives Patients with osteoarthritis (OA) affecting multiple joints have poorer health outcomes than those without, yet most research examines isol...
Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, y...
Malawi's HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 mo...
Neural Architecture Search (NAS) for object detection is severely bottlenecked by high evaluation cost, as fully training each candidate YOLO architec...
Measuring the growth rate of filamentous fungi is an essential phenotype assay in fungal biology, enabling the comparison of nutrient-related fitness ...
Stress detection with wearable physiological sensors is vital in digital health and affective computing. Conventional machine learning techniques usua...
Background Personalized medicine promises to tailor treatments to the individual, but it carries a hidden risk: mistaking statistical noise for action...
Background: Cardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application de...
Objective: To evaluate modifiable antepartum and intrapartum factors associated with nulliparous, term, singleton, vertex (NTSV) cesarean delivery and...
Introduction: Recreational and medical cannabis use (CU) information is often available within the electronic health record (EHR) in a format that is ...
Introduction: Physical fatigue is a key determinant of operational readiness in the physically demanding occupations of tactical athletes. Specific ho...
To empower the iterative assessments involved during a person's rehabilitation, automated assessment of a person's abilities during daily activities r...
The discovery of novel catalysts tailored for particular applications is a major challenge for the twenty-first century. Traditional methods for this ...
This work introduces a novel training paradigm that draws from affective neuroscience. Inspired by the interplay of emotions and cognition in the huma...
Activity of the mesolimbic system is essential for adaptive performance of reward-related behaviors. Within this system, dopaminergic (DAergic) neuron...
Societies are aging rapidly in parallel with the increasingly earlier onset of serious diseases in younger populations. These and other factors are cr...