AIMC Topic: Young Adult

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Technical Note: Using Machine Learning to Predict Locomotor Behavior in Great Apes and Humans From Femur Metaphyseal Shape.

American journal of biological anthropology
OBJECTIVES: The morphology of the hominoid distal femoral metaphyseal surface has been demonstrated to reflect locomotor behavior throughout ontogeny. Here, we quantify metaphyseal surface morphology to evaluate its predictive relationship to locomot...

Machine-learning models to predict iron recovery after blood donation: a model development and external validation study.

The Lancet. Haematology
BACKGROUND: Machine-learning models directly predicting iron biomarkers after blood donation could help to manage donation-associated iron deficiency and avoid low haemoglobin deferrals. No such models have been externally validated internationally. ...

Artificial intelligence-powered smart vision glasses for the visually impaired.

Indian journal of ophthalmology
PURPOSE: In India, 4.80 million people are blind, and 4.69 million have severe visual impairment. Globally, the digital era and the advent of artificial intelligence devices offer solutions for daily challenges faced by the visually impaired, but the...

An explainable machine learning framework for predicting driving states using electroencephalogram.

Medical engineering & physics
OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological dis...

Robot or human musicians? The modulating role of perceived performer on how music influences food choices.

Applied psychology. Health and well-being
Previous research has shown that music robots may reshape people's perceptions of music and health-related behaviors. We investigated how the perceived identity of the music performers (humans or robots) influenced people's music-induced mental image...

Artificial intelligence-enhanced 3D gait analysis with a single consumer-grade camera.

Journal of biomechanics
Gait analysis is crucial for diagnosing and monitoring various healthcare conditions, but traditional marker-based motion capture (MoCap) systems require expensive equipment, extensive setup, and trained personnel, limiting their accessibility in cli...

Empirically Transformed Energy Patterns: A novel approach for capturing fNIRS signal dynamics in pain assessment.

Computers in biology and medicine
The accurate assessment of pain in clinical settings is challenging due to its subjective nature. In this study, we used functional near-infrared spectroscopy (fNIRS) to measure brain activity by detecting changes in blood oxygenation. Leveraging the...

Prediction of endoscopic restenosis after endoscopic balloon dilation in patients with Crohn's disease: a machine learning approach.

Surgical endoscopy
BACKGROUND: Endoscopic balloon dilation (EBD) is recognized as a minimally invasive and effective procedure for managing intestinal stenosis in patients with Crohn's disease (CD). It offers an alternative to surgery and has been shown to improve the ...

Predicting Field-Sport Distances Without Global Positioning Systems in Indoor Play: A Comparative Study of Machine-Learning Techniques.

International journal of sports physiology and performance
PURPOSE: Accurately predicting the distance covered by athletes during indoor sport activities without the use of GPS (global positioning systems) presents a significant challenge. This study evaluates the effectiveness of various machine-learning te...

Concise multi-class anxiety disorder risk assessment: A novel advanced machine learning approach.

Journal of anxiety disorders
Rapidly assessing anxiety disorder risk is crucial for effective mental health screen and intervention. However, traditional survey tools such as DASS-42 are time-consuming in responding and scoring. We used a novel advanced machine learning approach...