AutoHydrate: A wearable hydration monitoring system
Water is a highly abundant nutrient in the human body and monitoring of its regulation is essential to keep the body hydrated. A number of critical health conditions including swelling of the brain and short/long term memory loss are associated with poor or excessive drinking habits. This can be prevented with the use of a real time hydration monitoring system. In this paper we presented AutoHydrate, a wearable hydration monitoring system which continuously monitors the drinking activities and daily fluid requirements of the user through automatic detection of drinking and body activities. The system is built using a throat microphone for collecting acoustic signals, a smartwatch for collecting body activity, an embedded computer for processing the signals and sending recommendation to a smartphone app in real time for an interactive information display. After different time, frequency and cepstral domain features are extracted from the signals, drinking activities are classified using Support Vector Machine (SVM) and body activity is classified using Gradient Boosting Decision Tree algorithm. The Dietary Reference Intake standard is followed for recommending the amount of fluid required using our detection. Based on our experimental results on 8 subjects, a Drinking detection accuracy of 91.5% and Body activity classification accuracy of 89.12% are obtained. Results show that our system is feasible for real time monitoring of body hydration.