51. Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature
- Author
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Wang, Shu, Rovira, Meritxell, Demuru, Silvia, Lafaye, Ce´line, Kim, Jaemin, Paul Kunnel, Brince, Besson, Cyril, Fernandez-Sanchez, Cesar, Serra-Graells, Francisco, Margarit-Taule´, Josep Maria, Aymerich, Joan, Cuenca, Javier, Kiselev, Ilya, Gremeaux, Vincent, Saubade, Mathieu, Jiménez-Jorquera, Cecilia, Briand, Danick, and Liu, Shih-Chii
- Subjects
core body temperature prediction ,machine learning ,sweat wearable patch ,printed sensors ,ISFET sensors ,sweat biomonitoring platform - Abstract
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 incorporat- ing 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.