  Staying hydrated and drinking fluids is extremely crucial to stay healthy and
maintaining even basic bodily functions. Studies have shown that dehydration
leads to loss of productivity, cognitive impairment and mood in both men and
women. However, there are no such an existing tool that can monitor dehydration
continuously and provide alert to users before it affects on their health. In
this paper, we propose to utilize wearable Electrodermal Activity (EDA) sensors
in conjunction with signal processing and machine learning techniques to
develop first time ever a dehydration self-monitoring tool, \emph{Monitoring My
Dehydration} (MMD), that can instantly detect the hydration level of human
skin. Moreover, we develop an Android application over Bluetooth to connect
with wearable EDA sensor integrated wristband to track hydration levels of the
users real-time and instantly alert to the users when the hydration level goes
beyond the danger level. To validate our developed tool's performance, we
recruit 5 users, carefully designed the water intake routines to annotate the
dehydration ground truth and trained state-of-art machine learning models to
predict instant hydration level i.e., well-hydrated, hydrated, dehydrated and
very dehydrated. Our system provides an accuracy of 84.5% in estimating
dehydration level with an sensitivity of 87.5% and a specificity of 90.3% which
provides us confidence of moving forward with our method for larger
longitudinal study.
