A quantitative, multimodal wearable bioelectronic device for comprehensive stress assessment and sub-classification

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Health Monitoring

Background:

• Stress significantly impacts mental and physical health.
• Current methods lack objective, continuous, long-term stress monitoring tools.
• Understanding stress requires precise physiological and molecular biomarker data.

Purpose of the Study:

• Introduce SQC-SAS, a multimodal wearable device for continuous stress monitoring.
• Enable quantitative stress assessment and sub-classification using multiple biomarkers.
• Advance precise, objective stress monitoring for personalized interventions.

Main Methods:

• Developed a multimodal wearable device (SQC-SAS) for simultaneous biomarker measurement.
• Ensured device stability, reusability, and wireless data/power transmission.
• Utilized machine learning for data-driven stress assessment and classification.

Main Results:

• SQC-SAS continuously measures multiple physiological and molecular stress biomarkers.
• The device supports quantitative stress assessment and sub-classification.
• Machine learning correlates biomarker profiles with different stress states.

Conclusions:

• SQC-SAS offers a precise, objective solution for continuous stress monitoring.
• The wearable enables real-time visualization and personalized interventions.
• This technology lays the foundation for deeper understanding of stress-related outcomes.