ArticleSensors (Basel, Switzerland)2022
Towards On-Device Dehydration Monitoring Using Machine Learning from Wearable Device's Data.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- PSAML: A Methodological Approach for Noninvasive Computerized Hydration Level Estimation.Sensors (Basel, Switzerland) · 2026Article
- RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.Sensors (Basel, Switzerland) · 2025Review
- Wireless Soft Athlete Bioelectronics for Monitoring Carbon Dioxide Ventilation and Physiological Performance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions.JMIR mHealth and uHealth · 2025Article
- A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024Review
- Intensive, Real-Time Data Collection of Psychological and Physiological Stress During a 96-Hour Field Training Exercise at a Senior Military College: Feasibility and Acceptability Cohort Study.JMIR formative research · 2024Article
- Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features.PloS one · 2024Article
- Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis.Sensors (Basel, Switzerland) · 2022Article
- Advancing Digital Medicine with Wearables in the Wild.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning for Healthcare Wearable Devices: The Big Picture.Journal of healthcare engineering · 2022Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.