Key Takeaways & Executive Findings
- •• • Classification accuracy for eyelash guidance signals exceeds 98.5% using a CNN-LSTM hybrid model, enabling reliable, low-latency human-machine interaction without optical tracking hardware. • • The PF-TENG device employs PDMS-FDTS thin films, achieving ultra-flexibility and transparency that permit attachment to eyeglasses without visual obstruction, a critical requirement for continuous driver monitoring. • • Real-time fatigue detection capability was validated by monitoring ocular states, with the system accurately identifying fatigue signs and feeding back driver fatigue levels, directly addressing safety-critical scenarios in intelligent driving. • • The system eliminates dependence on video recording, infrared tracking, and image recognition, reducing hardware complexity and power consumption while maintaining high classification performance, thus offering a cost-effective alternative to conventional eye-tracking systems.
Abstract
Conventional eye-movement interaction systems depend on video capture, infrared tracking, and image recognition, which impose inherent constraints on accuracy, response latency, and stability. This study introduces an eyelash-guided signal interaction system based on a triboelectric nanogenerator (PF-TENG) using PDMS-FDTS thin films. The system employs eyelash movements as interactive inputs, eliminating the need for complex optical acquisition devices. A CNN-LSTM hybrid neural network classifies distinct eyelash movement patterns with a classification accuracy exceeding 98.5%. The PF-TENG device exhibits ultra-flexibility and transparency, enabling seamless integration onto eyeglasses without obstructing the user's field of view. Experimental validation demonstrates real-time monitoring of ocular states for driving fatigue detection, accurately identifying fatigue signs and enhancing application potential in intelligent driving. The system offers a natural, comfortable input modality and significant advantages for human-machine interaction, with broad prospects in eye-movement control and intelligent transportation.
1. Introduction
Eye-movement interaction technology has emerged as a low-energy, high-efficiency paradigm for intelligent human-machine interaction. However, commercial systems remain tethered to video capture, infrared tracking, and image recognition pipelines, which suffer from accuracy degradation under variable lighting, response latency from frame processing, and stability issues in dynamic environments. These limitations have stalled widespread adoption in safety-critical applications such as driving fatigue detection, where real-time, unobtrusive monitoring is paramount.
This study presents an eyelash-guided signal interaction system based on a triboelectric nanogenerator (PF-TENG) using PDMS-FDTS thin films. By transducing eyelash movements directly into electrical signals, the system bypasses optical acquisition entirely, reducing hardware complexity and power demand. A CNN-LSTM hybrid neural network classifies these signals with >98.5% accuracy, enabling natural, comfortable input. The ultra-flexible, transparent device integrates onto eyeglasses without obstructing vision, and its application to real-time driver fatigue detection demonstrates a practical pathway to enhance driving safety and human-machine interaction.
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DAI Jingyun, LI Long, LYU Jiayang, SHEN Yifan, GUO Yanming, DU Lingfeng, ZHOU Xiongtu, ZHANG Yongai, GUO Tailiang, WU Chaoxing (2026). Ultra-flexible Transparent Self-powered Triboelectric Sensors for Eyelash-Guided Human-Machine Interaction. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-026-4467-x
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Frequently Asked Questions
What is the measured classification accuracy of the CNN-LSTM model for eyelash guidance signals, and how does it compare to conventional eye-tracking systems?
The CNN-LSTM hybrid neural network achieves a classification accuracy exceeding 98.5% for different typical eyelash movements. This surpasses many video-based eye-tracking systems, which often report accuracies below 95% under variable lighting and head motion, and eliminates the latency associated with image processing.
How does the PF-TENG device address the trade-off between transparency, flexibility, and triboelectric output for on-eyeglass integration?
The device utilizes PDMS-FDTS thin films that are both ultra-flexible and transparent, allowing conformal attachment to eyeglasses without obstructing the user's vision. While specific output metrics (e.g., voltage, current) are not detailed in the provided text, the successful >98.5% classification demonstrates sufficient signal-to-noise ratio for reliable operation.
What are the failure mechanisms or degradation risks for the PDMS-FDTS triboelectric layer under continuous mechanical cycling from eyelash movements?
The provided text does not specify cycling durability data. However, PDMS-based triboelectric layers typically exhibit stable performance over thousands of cycles; long-term degradation may arise from surface wear, contamination, or humidity effects, which require further accelerated aging tests to quantify.
Can the system maintain >98.5% accuracy across diverse users with varying eyelash lengths, blink rates, and facial geometries?
The study reports >98.5% accuracy for the tested cohort, but cross-user generalizability is not explicitly quantified. The CNN-LSTM model's performance may vary with individual eyelash characteristics; adaptive calibration or transfer learning would be necessary for robust deployment across broader populations.
What is the response latency of the PF-TENG sensor and CNN-LSTM pipeline for real-time fatigue detection, and how does it compare to infrared eye-tracking?
The text does not provide explicit latency values. However, the direct triboelectric transduction eliminates image capture and processing delays inherent in infrared systems, potentially reducing end-to-end latency to the millisecond range, which is critical for real-time fatigue alerts.
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