AIMC Topic: Exercise

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Design of an Interactive Exercise and Leisure System for the Elderly Integrating Artificial Intelligence and Motion-Sensing Technology.

Sensors (Basel, Switzerland)
In response to the global trend of population aging, the issue of providing elderly individuals suitable leisure and entertainment has become increasingly important. In this study, it aims to utilize artificial intelligence (AI) technology to offer t...

Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model.

Frontiers in public health
INTRODUCTION: This study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and exercise behaviors through personalized public health interventions.

Enhancing smart healthcare with female students' stress and anxiety detection using machine learning.

Psychology, health & medicine
Machine learning (ML) is widely used to predict and detect stress and anxiety. Early detection of stress or anxiety is crucial for clinical pathways to enhance the supportive environment in society, particularly among female students. This study aims...

Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances.

Respiratory physiology & neurobiology
Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, there has been a renewed interest in uncovering the mechanisms underlying this distressing symptom. We start by articulating a conceptual framework linki...

Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis.

Advances in physiology education
Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives article explores how integrating G...

Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.

JMIR mHealth and uHealth
BACKGROUND: Wearable technology is used by consumers worldwide for continuous activity monitoring in daily life but more recently also for classifying or predicting mental health parameters like stress or depression levels. Previous studies identifie...

Determining the Importance of Lifestyle Risk Factors in Predicting Binge Eating Disorder After Bariatric Surgery Using Machine Learning Models and Lifestyle Scores.

Obesity surgery
BACKGROUND: This study was conducted to assess the association between lifestyle risk factors (LRF) and odds of binge eating disorder (BED) 2 years post laparoscopic sleeve gastrectomy (LSG) using lifestyle score (LS) and machine learning (ML) models...

Comparison of predictive models for knee pain and analysis of individual and physical activity variables using interpretable machine learning.

The Knee
BACKGROUND: Knee pain is associated with not only individual factors such as age and obesity but also physical activity factors such as occupational activities and exercise, which has a significant impact on the lives of adults and the elderly.

Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study.

JMIR aging
BACKGROUND: Depression, characterized by persistent sadness and loss of interest in daily activities, greatly reduces quality of life. Early detection is vital for effective treatment and intervention. While many studies use wearable devices to class...