AI AND MACHINE LEARNING APPLICATIONS IN PERSONALIZED HYDRATION AND FLUID REPLACEMENT RECOMMENDATIONS

Gunashree T K, Tejaswini N, Nagarjuna N V, Harshitha G, Suhas S

Individualized hydration is central to sustaining physiological balance, improving physical fitness, and reducing the potential dangers of dehydration on one's health. Conventional approaches to measuring one's hydration status through changes in body mass or urinalysis often prove ineffective since they lack the capacity to deliver immediate, customized information about the individual's hydration status. Recent advances in AI and ML have opened up the possibility of building more advanced data-driven systems that can monitor an individual's hydration status continuously and offer customized advice on fluid replacement.
Through the use of wearable sensors, biosensors, and mobile health applications, these advanced models utilize advanced mathematical algorithms to evaluate an individual's unique physiological and environmental factors to forecast one's hydration requirements. Although the use of deep learning models and other hybrid approaches has greatly enhanced the precision of these predictions by recognizing complicated patterns in time series data, there is still room for improvement with regard to issues of data validity, standardization, and generalizability. The present review offers an in-depth examination of recent AI-driven hydration prediction models, assesses their predictive methodologies, and highlights crucial gaps in existing research.

Keywords –Individualized hydration, Machine learning, Artificial intelligence, Wearable sensors, Fluid replacement, Biosensors.