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Updated: May 21, 2025
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
Published on: January 22, 2018
Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional
Hodam Kim1, Hojoong Kim2, Yoon Jae Lee3
1Division of Biomedical Engineering, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Republic of Korea; BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Wearable Intelligent Systems and Healthcare Center, Institute for Matter and Systems, Georgia Institute of Technology, Atlanta, GA 30332, USA.
A new wireless wearable device can now continuously monitor brain and cardiorespiratory signals to detect fatigue, stress, and sleep quality in real-time. This technology aims to enhance personalized digital healthcare and at-home health monitoring.
Area of Science:
- Biomedical Engineering
- Digital Healthcare
- Wearable Technology
Background:
- Accurate measurement of mental states like fatigue, stress, and sleep is crucial for personalized digital healthcare.
- These states are interconnected, forming feedback loops that negatively impact cognition, behavior, and recovery.
- Existing methods often lack continuous monitoring capabilities or user comfort.
Purpose of the Study:
- To introduce a wireless, soft, multifunctional bioelectronic system for continuous, real-time detection and management of mental states.
- To demonstrate the system's ability to capture clinical-grade brain and cardiorespiratory signals with high fidelity.
- To develop algorithms for automated classification of drowsiness, stress, and sleep quality.
Main Methods:
- Development of a soft, flexible, and reusable membrane biopatch worn on the forehead.
- Integration of sensors to measure electroencephalogram (EEG), electrooculogram (EOG), pulse rate, and blood oxygen saturation.
- In vivo studies with human subjects to assess device performance during various activities and sleep.
- Application of advanced signal processing and deep learning algorithms for data analysis and classification.
Main Results:
- The bioelectronic system demonstrated excellent skin-conformal contact and minimal motion artifacts.
- Clinical-quality physiological data (EEG, EOG, pulse rate, SpO2) were captured effectively, even during sleep.
- Automated, real-time classification of driving drowsiness, stress conditions, and sleep quality was achieved using developed algorithms.
- The wearable device proved imperceptible and comfortable for users during continuous monitoring.
Conclusions:
- The developed soft bioelectronic system offers a promising solution for continuous, real-time monitoring of mental states.
- This technology has the potential to significantly advance personalized digital healthcare, particularly in at-home health monitoring and management of fatigue, stress, and sleep.
- The system's comfort, reusability, and clinical-grade data acquisition pave the way for widespread adoption in healthcare.
