Energy Optimization Techniques for Smart Wearable Computing in Textile Industries

Smart wearable computing integrated into textile industries is emerging as a transformative technology for healthcare, fitness, and industrial monitoring applications. However, the continuous collection, processing, and transmission of sensor data in smart fabrics lead to significant energy consumption, which directly impacts system performance, durability, and user adoption. This research presents energy optimization techniques that combine lightweight machine learning models, reinforcement learning-based power scheduling, and adaptive duty cycling to improve the efficiency of smart wearable systems. The proposed framework incorporates federated learning for on-device training to reduce cloud communication and energy harvesting modules to extend battery life. Experimental evaluation on textile-integrated sensor prototypes demonstrates up to 42% reduction in power consumption, while maintaining an average model accuracy of 95.8% for activity recognition and 96.2% for physiological monitoring tasks. Compared to baseline wearable systems, the approach improves system uptime by 37% and sustains real-time data processing under constrained power budgets. These results indicate that energy-efficient algorithms are crucial for scaling next-generation wearable computing in textile industries, ensuring both high accuracy and sustainable performance for real-world applications.