Multisensing Wearables for Real-Time Monitoring of Sweat Electrolyte Biomarkers During Exercise and Analysis on Their Correlation With Core Body Temperature Article Swipe
Shu Wang
,
Meritxell Rovira
,
Silvia Demuru
,
Céline Lafaye
,
Jaemin Kim
,
Brince Paul Kunnel
,
Cyril Besson
,
César Fernández‐Sánchez
,
Francisco Serra-Graells
,
Josep Maria Margarit
,
Joan Aymerich
,
Javier Cuenca
,
Ilya Kiselev
,
Vincent Grémeaux
,
Mathieu Saubade
,
Cecilia Jiménez‐Jorquera
,
D. Briand
,
Shih‐Chii Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tbcas.2023.3286528
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tbcas.2023.3286528
Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥ 0.65) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tbcas.2023.3286528
- OA Status
- green
- Cited By
- 19
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380839095
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380839095Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tbcas.2023.3286528Digital Object Identifier
- Title
-
Multisensing Wearables for Real-Time Monitoring of Sweat Electrolyte Biomarkers During Exercise and Analysis on Their Correlation With Core Body TemperatureWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-15Full publication date if available
- Authors
-
Shu Wang, Meritxell Rovira, Silvia Demuru, Céline Lafaye, Jaemin Kim, Brince Paul Kunnel, Cyril Besson, César Fernández‐Sánchez, Francisco Serra-Graells, Josep Maria Margarit, Joan Aymerich, Javier Cuenca, Ilya Kiselev, Vincent Grémeaux, Mathieu Saubade, Cecilia Jiménez‐Jorquera, D. Briand, Shih‐Chii LiuList of authors in order
- Landing page
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https://doi.org/10.1109/tbcas.2023.3286528Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- Concepts
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SWEAT, Wearable computer, Biomarker, Photoplethysmogram, Biomedical engineering, Computer science, Medicine, Chemistry, Internal medicine, Embedded system, Filter (signal processing), Computer vision, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 8, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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