AIMC Topic: Exercise
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Development and evaluation of the COntextualised and Personalised Physical activity and Exercise Recommendations (COPPER) Ontology.
The international journal of behavioral nutrition and physical activity
May 5, 2025
BACKGROUND: Personalised recommendations for action and coping plans for physical activity (PA) may reduce user burden and increase plan quality. Ontologies are a promising alternative to existing black-box approaches for creating such personalised r...
A study on factors influencing digital sports participation among Chinese secondary school students based on explainable machine learning.
Scientific reports
May 5, 2025
This study utilized data from 4,925 Hong Kong students in the 2018 Programme for International Student Assessment (PISA) to investigate factors influencing secondary school students' use of digital devices for sports participation and their threshold...
Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems.
Computer methods and programs in biomedicine
Apr 23, 2025
BACKGROUND AND OBJECTIVE: This study introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises while minimizing performance dispa...
Calibration and Validation of Machine Learning Models for Physical Behavior Characterization: Protocol and Methods for the Free-Living Physical Activity in Youth (FLPAY) Study.
JMIR research protocols
Apr 16, 2025
BACKGROUND: Wearable activity monitors are increasingly used to characterize physical behavior. The development and validation of these characterization methods require criterion-labeled data typically collected in a laboratory or simulated free-livi...
Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data.
Journal of medical systems
Apr 9, 2025
The increasing availability of wearable device data provides an opportunity for developing personalized models for health monitoring and condition prediction. Unlike conventional approaches that rely on pooled data from diverse individuals, our study...
Design of an Interactive Exercise and Leisure System for the Elderly Integrating Artificial Intelligence and Motion-Sensing Technology.
Sensors (Basel, Switzerland)
Apr 5, 2025
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
Apr 4, 2025
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.
Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.
BMJ open
Mar 22, 2025
OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).
Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis.
Advances in physiology education
Mar 13, 2025
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
Mar 6, 2025
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...
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