Graphene-Based Wearable E-Textiles Integrated with Deep Learning for Real-Time Sweat Analysis in Military and Sports Applications

Graphene-based conductive fabrics integrated with deep learning algorithms enable real-time sweat analysis for military and sports applications. These graphene-based e-textiles, renowned for their flexibility, durability, and bio-signal capturing capabilities, are screen-printed onto fabric to create non-invasive sweat sensors. The sensors measure physiological data such as electrolyte balance, hydration levels, and sweat rates, essential for monitoring physical performance under various conditions. A deep learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, processes the collected data to classify sweat composition and recommend optimal clothing based on environmental and physical stressors. Performance analysis demonstrates CNN's superior convergence speed and lower loss values compared to LSTM. This integrated system offers real-time feedback, helping to prevent dehydration, fatigue, and heat-related illnesses, thus providing a transformative approach to personalized healthcare and performance monitoring in demanding environments.