ArticleScientific reports2022
Personalized wearable electrodermal sensing-based human skin hydration level detection for sports, health and wellbeing.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- A full-range sweat rate sensor with superhydrophobic heterophase shunt for psychology, skin health and body dehydration evaluating.Science advances · 2026Article
- PSAML: A Methodological Approach for Noninvasive Computerized Hydration Level Estimation.Sensors (Basel, Switzerland) · 2026Article
- HTO-UWSN: a hierarchical task offloading protocol for underwater wireless sensor networks in aquatic sports monitoring systems.Scientific reports · 2026Article
- Evaluating the Impact of hydration on electrodermal activity: A Comparison between AC bioimpedance and DC skin potential metrics.Journal of electrical bioimpedance · 2026Article
- Human bioimpedance-based state detection technologies for sports health monitoring: a review.Frontiers in physiology · 2026Review
- AI-Driven Wearable Bioelectronics in Digital Healthcare.Biosensors · 2025Review
- Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions.JMIR mHealth and uHealth · 2025Article
- Digital health interventions from the humanistic perspective of sports: strategies to promote health for all.Frontiers in public health · 2025Article
- Non-invasive approaches to hydration assessment: a literature review.Urolithiasis · 2024Review
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.Journal of personalized medicine · 2024Review
- Green and Integrated Wearable Electrochemical Sensor for Chloride Detection in Sweat.Sensors (Basel, Switzerland) · 2022Article
- Pilot Study of a Wearable Hydration Monitor in Haemodialysis Patients: Haemodialysis Outcomes & Patient Empowerment Study 02.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized hydration level monitoring play vital role in sports, health, wellbeing and safety of a person while performing particular set of activities. Clinical staff must be mindful of numerous physiological symptoms that identify the optimum hydration specific to the person, event and environment. Hence, it becomes extremely critical to monitor the hydration levels in a human body to avoid potential complications and fatalities. Hydration tracking solutions available in the literature are either inefficient and invasive or require clinical trials. An efficient hydration monitoring system is very required, which can regularly track the hydration level, non-invasively. To this aim, this paper proposes a machine learning (ML) and deep learning (DL) enabled hydration tracking system, which can accurately estimate the hydration level in human skin using galvanic skin response (GSR) of human body. For this study, data is collected, in three different hydration states, namely hydrated, mild dehydration (8 hours of dehydration) and extreme mild dehydration (16 hours of dehydration), and three different body postures, such as sitting, standing and walking. Eight different ML algorithms and four different DL algorithms are trained on the collected GSR data. Their accuracies are compared and a hybrid (ML+DL) model is proposed to increase the estimation accuracy. It can be reported that hybrid Bi-LSTM algorithm can achieve an accuracy of 97.83%.
Indexed as
Identifiers
What OpenQuestion holds
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.