
An AI-based algorithm for analyzing physical activity and health-related fitness in youth.
AI-driven analysis boosts youth fitness assessments: 98.448% accuracy in classification and enhanced performance predictions. ππ€
Discover the newest research about AI innovations in ποΈ Fitness & Sports.

AI-driven analysis boosts youth fitness assessments: 98.448% accuracy in classification and enhanced performance predictions. ππ€

AI telerehabilitation boosts exercise capacity in hypertension patients: 62.77m improvement in 6MWD after 8 weeks! ππͺ

Health tech suppliers predict significant advancements in NHS technology by 2026, focusing on AI integration, patient-centered care, and improved data management. π₯π»

Exploring biological age through omics: Genomics, epigenomics, and microbiomics reveal insights into aging and health. π§¬π¬

Personalized fitness via machine learning: XGBoost shows 0.789 MeanIoU, enhancing public health strategies. ππ€

SHAP analysis reveals key injury risk factors in university football players: stress, sleep, and balance. β½π

Fitness apps may have negative effects on users, leading to feelings of shame and disengagement. πββοΈπ

Innovative CPF-TOPSIS model enhances decision-making in speech and sports training, improving accuracy by addressing uncertainty. ππ€ποΈββοΈ

Efficient walking energy expenditure estimation via continuous ramp protocol shows 10.7% error, outperforming traditional methods. πΆββοΈπ

New wearable device monitors muscle health in older adults. π¦΅π Aims to detect sarcopenia for timely intervention.