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Emerging Multifunctional Wearable Sensors: Integrating Multimodal Sweat Analysis and Advanced Material Technologies
Chong-Bo Ma1, Xudong Shang2,3, Mimi Sun1
1Key Laboratory of Polyoxometalate and Reticular Material Chemistry of Ministry of Education, Key Laboratory of Nanobiosensing and Nanobioanalysis at Universities of Jilin Province Analysis and Testing Center, Department of Chemistry, Northeast Normal University, Changchun, Jilin 130024, China.
ACS Sensors
|March 31, 2025
Summary
Multimodal wearable sensors analyze sweat biomarkers for real-time health monitoring. These advanced sensors integrate multiple functions for comprehensive wellness tracking.
Area of Science:
- Biomedical Engineering
- Materials Science
- Analytical Chemistry
Background:
- Sweat analysis offers noninvasive, real-time physiological state monitoring via electrolytes, metabolites, and hormones.
- Multimodal wearable sensors integrate chemical, physical, and thermal sensing for comprehensive biomarker and physiological signal capture.
Purpose of the Study:
- To systematically review the classification, design, and applications of multimodal wearable sensors for health monitoring.
- To highlight innovations in functional materials, self-powered systems, and energy harvesting for continuous monitoring.
Main Methods:
- Exploration of advanced materials (hydrogels, MXenes, graphene) and structural designs for sensor fabrication.
- Analysis of sensor integration for simultaneous monitoring of biomarkers (glucose, sodium, cortisol) and physical parameters (movement, temperature).
Main Results:
- Multimodal sensors enable real-time monitoring of diverse biomarkers and physiological signals.
- Innovations in conductive hydrogels and biomimetic structures enhance sensor performance.
- Self-powered systems and energy harvesting are crucial for continuous monitoring.
Conclusions:
- Multimodal wearable sensors offer transformative potential for personalized healthcare and proactive wellness management.
- Future directions include scalable manufacturing, AI integration, and standardized frameworks for enhanced sensor adoption.
