Ultra-flexible Transparent Self-powered Triboelectric Sensors for Eyelash-Guided Human-Machine Interaction
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.