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Electrodermal Sensing-Based Non-Invasive Context-Aware Dehydration Alert System Using Machine Learning Algorithm

EasyChair Preprint no. 8815

6 pagesDate: September 6, 2022


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 existing tools that can monitor dehydration continuously and
provide alert to users before it effects 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, 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 user’s 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.

Keyphrases: electrodermal activity, hydrated, self-monitoring

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {K T Tharun and P Raahul Prasath and P Mounaguru and S Kathirvarshan and R Sruthika},
  title = {Electrodermal Sensing-Based Non-Invasive Context-Aware Dehydration Alert System Using Machine Learning Algorithm},
  howpublished = {EasyChair Preprint no. 8815},

  year = {EasyChair, 2022}}
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