PREDICTIVE AND COMPUTATIONAL MODELS FOR SWEAT RATE, ELECTROLYTE LOSS AND HYDRATION STATUS

Mohammed Sahil Sinwan, A V Samanvitha Bhat, Raksha H D, Spandana S, Vindhu T H

Maintaining proper hydration is vital for physical performance, temperature control, and overall health, especially during exercise and in different environmental conditions. Traditional ways of checking hydration levels are often invasive, slow, or not suitable for real-time situations, which limits their usefulness in active scenarios. Recent improvements in wearable tech, sweat-based sensors, and computer modelling now allow for continuous tracking of physiological and biochemical factors like heart rate, sweat rate, and electrolyte levels. This study reviews and combines existing predictive and computational methods for estimating sweat rate, electrolyte loss, and hydration status. It focuses on machine learning models that use various types of data, including physiological signals and sweat markers, to provide accurate and personalized hydration assessments. The study also looks at advances in microfluidic and electrochemical sensing technologies and their role in enabling non-invasive, real-time data collection. The analysis shows that while important strides have been made in areas like sensing and predicting, there is still a gap in fully integrated systems that bring together data collection, processing, and smart recommendations. To fill this gap, the study highlights the promise of unified frameworks that use wearable devices and artificial intelligence to offer personalized hydration insights. These systems can improve performance, lower dehydration risks, and aid in proactive health management.

Keywords –Electrolyte Loss, Sweat Biomarkers, Machine Learning, Predictive Modelling, Physiological Signals, Real-Time Monitoring, Non-Invasive Sensing, Microfluidic Devices.