Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics

• Published in: Biosensors & bioelectronics Vol. 279; p. 117387
• Main Authors: Kim, Hodam, Kim, Hojoong, Lee, Yoon Jae, Yi, Hoon, Kwon, Youngjin, Huang, Yunuo, Trotti, Lynn Marie, Kim, Yun Soung, Yeo, Woon-Hong
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.07.2025
• Subjects: Adult; Biosensing Techniques - instrumentation; Continuous detection; Electroencephalography - instrumentation; Electrooculography; Equipment Design; Fatigue - diagnosis; Heart Rate; Humans; Male; Mental state monitoring; Monitoring, Physiologic - instrumentation; Sleep - physiology; Soft bioelectronics; Wearable Electronic Devices; Wireless sensors; Wireless Technology - instrumentation; Mental state monitoring; Soft bioelectronics; Wireless sensors; Continuous detection
• ISSN: 0956-5663; 1873-4235; 1873-4235
• DOI: 10.1016/j.bios.2025.117387

Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: persistent fatigue can induce cognitive stress, while chronic stress impairs sleep quality, creating a harmful feedback loop. Here, we introduce a wireless, soft, multifunctional bioelectronic system offering the continuous real-time detection and management of comprehensive mental states. The all-in-one wearable device, mounted on the forehead, measures clinical-grade brain and cardiorespiratory signals. This membrane biopatch is imperceptible, flexible, and reusable, providing ultimate user comfort while detecting high-fidelity electroencephalogram, electrooculogram, pulse rate, and blood oxygen saturation. A set of in vivo studies with human subjects demonstrates that the soft device has great skin-conformal contact and minimized motion artifacts, capturing clinical-quality data with different activities, even during sleep. The developed signal processing methods and deep-learning algorithms offer automated, real-time classification of driving drowsiness, stress conditions, and sleep quality. The bioelectronics platforms in this study have the potential to revolutionize digital healthcare, particularly personalized medicine and at-home health monitoring.