63 Taiwan Sporting Goods Manufacturers Association management and AI-based guidance, using wearables to monitor heart rate, steps, and posture—delivering tailored exercise and diet advice. AI-powered mirrors and virtual-coach apps can correct form and adjust intensity in real time. Beyond exercise, AI models can forecast injuries and chronic-disease risks, strengthening preventive medicine and optimizing training performance. Three major technological directions are emerging: 1. AI-Chip Integration and Edge Computing Embedded AI chips enable on-device data processing for low-latency, high-efficiency feedback and movement correction. This reduces cloud dependency, enhances data privacy, and allows offline operation. With rising chip performance and lower power consumption, edge-AI architectures will enable more advanced motion analysis and personalized training. 2. Digital-Twin Technology By creating a virtual replica of each user, digital twins simulate exercise scenarios and predict outcomes or risks. Real-time synchronization of physical and physiological data allows dynamic program optimization and injury prediction. This also enables remote coaching and rehabilitation, offering a personalized, intelligent health-management ecosystem. 3. Multi-Modal Training Platforms Integrating visual, auditory, motion, and physiological data streams, AI fusion analytics provide holistic feedback and natural human-machine interaction. Such systems deliver more accurate, adaptive, and engaging workouts, customized to each user’s needs. 4. Business Models and Industry Opportunities AI integration is transforming fitness from product-based sales to subscription-based ecosystems. By merging personalized training plans, remote coaching, and health-data analytics, brands can offer continuous value and build long-term engagement. Data-driven partnerships are emerging across insurance, corporate wellness, and social-fitness
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