Summary:
UCLA researchers from the Department of Bioengineering have developed an ultrathin, self-powered, and stretchable sensor that adheres directly to the upper eyelid to track fatigue levels in real time.
Background:
Fatigue-induced impairments have been widely recognized as major contributors to decreased performance, increased error rates, and serious accidents. However, accurately measuring fatigue in everyday settings remains a significant challenge. Existing solutions—such as self-reported fatigue scores, EEG-based systems, or computer vision approaches—are limited by subjectivity, obtrusiveness, high cost, or poor reliability in uncontrolled environments. Meanwhile, real-time and unobtrusive monitoring tools for physiological fatigue remain largely unavailable, despite the growing need in domains such as transportation, healthcare, defense, and consumer wellness. There remains an unmet need for wearable systems that can provide comfortable, continuous, and high-resolution, monitoring of fatigue without interfering with natural behaviors or requiring bulky equipment.
Innovation:
UCLA researchers led by Professor Jun Chen have developed a self-powered, ultrathin, ultrasoft, and ultra-stretchable magnetoelastic sensor (3UM) that seamlessly adheres to the upper eyelid. This wearable sensor captures eye-blink parameters such as blink duration, opening/closing time, peak amplitudes, and reopening delay with exceptional sensitivity (0.2 μA kPa⁻¹) and mechanical conformity (Young’s modulus of 200 kPa; stretchability up to 530%). The sensor generates high-fidelity electrical signals from natural eyelid movements, enabling continuous fatigue monitoring without the need for external power. Paired with a one-dimensional convolutional neural network, the system analyzes six critical eye-blink metrics and classifies fatigue states with an accuracy of 96.4%.
Moreover, the sensor is integrated into a closed-loop fatigue management system that not only detects signs of fatigue but also supports responsive interventions. This technology offers a compelling, minimally invasive solution for fatigue diagnostics, suitable for long-term daily wear for a broad range of use cases such as transportation safety (e.g., monitoring drowsy drivers), workplace health in high-risk industries, and continuous wellness tracking in consumer wearables.
Potential Applications:
• Driver and pilot fatigue monitoring
• Military and defense performance assessment
• Worker safety in industrial and logistics settings
• Consumer wellness wearables
• Continuous patient monitoring in healthcare
• Sports and training optimization
• Real-time human-machine interface system
Advantages:
• Non-invasive, skin-conformal placement on the eyelid
• Real-time electrical signal generation from eye movement
• Self-powered operation for continuous sensing
• High mechanical flexibility and stretchability
• High fatigue detection accuracy
• Enables long-term, daily use in real-world settings
• Integrates with AI-based analytics and feedback systems
State of Development:
Sensor performance has been validated on human subjects.
Related Papers:
A manuscript was submitted to the journal Nature Electronics, and it is currently under review. It will be accepted soon and will be published in 1 month.
Xu, Jing, et al. “Quantitatively decoding fatigue levels with a self-powered on-eyelid magnetoelastic sensor.” Nature Electronics. Under Review.
Reference:
UCLA Case No. 2025-110
Lead Inventor:
Jun Chen, Department of Bioengineering