ENTERPRISE AI ANALYSIS
The multiple uses of artificial intelligence in exercise programs: a narrative review
Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications.
Key Metrics at a Glance
This narrative review explores the burgeoning role of artificial intelligence (AI) across physical activity, training, exercise, and health programs. Key findings reveal that AI, including machine learning and deep learning techniques, is being employed in diverse functionalities: generating automatic messages and user communication, predictive analytics for health outcomes and adherence, gesture and posture recognition, data collection via smart wearables, and as a guided training system. These applications demonstrate AI's significant potential to enhance physical activity across various demographics, from children to older adults and individuals with disabilities. The integration of AI offers innovative solutions for personalized and effective health interventions, addressing a critical gap in understanding practical applications.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Texting Messages & Notifications
Predictive Models
Gesture & Posture Recognition/Control Systems
Data Collection
Guided Trainers
AI is used to send tailored messages, feedback, and notifications to users via apps to encourage physical activity, manage health conditions, and provide recommendations based on self-monitored data. This includes systems for improving pain/stiffness, dietary advice, and personalized self-management plans.
AI models, including machine learning classification and explainable AI (XAI), are used to predict health outcomes like frailty and sarcopenia reversal, and to predict adherence to physical activity goals. These models leverage data to forecast individuals' responses to interventions and identify at-risk populations.
AI, utilizing deep learning (e.g., BPNN, CNN), is applied to analyze and recognize body movements for performance improvement in sports, rehabilitation, and health-oriented gaming. It also functions as a control system for devices like upper limb prostheses, enhancing training efficiency and user interaction.
AI-powered smart bracelets and wearables are used to collect and track physiological data during exercise, providing users with real-time insights into their physical status, including distance, time spent exercising, sleep, and calorie consumption.
AI-guided training systems (e.g., Tonal trainer) estimate and suggest resistance levels, monitor performance variables (speed, range of motion, power), and provide real-time feedback and safety supervision in exercise programs. This ensures personalized and adaptive training based on individual performance.
Enterprise Process Flow
1772 Studies Identified
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Duplicates Removed (1004 screened)
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Title & Abstract Screening (24 for full-text)
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Full-text Review (15 Included)
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LBP Disability Reduction (High-Intensity Pain)
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AI-Driven Sarcopenia & Frailty Reversal Prediction
Studies utilized Explainable Artificial Intelligence (XAI) and machine learning classification models to predict sarcopenia and frailty reversal. Wei et al. (50) achieved 85.7% accuracy in predicting sarcopenia reversal. Handgrip strength and 10m walking speed were identified as major contributors to predictive performance, guiding targeted interventions for physically frail older adults (47, 50, 51).
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