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

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Authors: Xiaochang Pei, Anita Ghandehari, Shingirirai Chakoma, Jerome Rajendran, Jorge Alfonso Tavares-Negrete, Rahim Esfandyarpour
Cited by: 20
References: 75

Abstract

Stress is a universal experience impacting mental and physical health. However, no precise, objective wearable tool exists for continuous, long-term stress monitoring, which is essential for understanding stress-related health outcomes. To address this gap, we introduce SQC-SAS, a multimodal wearable device that simultaneously and continuously measures multiple physiological and molecular stress biomarkers for quantitative stress assessment and sub-classification. This device features exceptional environmental stability, reusability, and fully wireless data and power operation. Machine learning enables data-driven stress assessment and classification across multiple stress states, allowing biomarker profiles to be correlated with each state. Its wristband-like design enables continuous stress monitoring and real-time visualization. We envision our wearable will greatly advance precise, objective stress assessment and monitoring, offering unprecedented capabilities and laying the foundation for personalized interventions and a deeper understanding of stress-related outcomes.

Fields of Study & Subjects

Advanced Sensor and Energy Harvesting Materials Emotion and Mood Recognition Mental Health via Writing

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Pages
1150–1150
Publication date
29 January 2026
Language
EN