Self-powered flexible handwriting input panel with 1D output enabled by convolutional neural network

The growing needs for wearable electronics urge the development of smart human-machine interfaces. Multi -output channels are required for current flexible input panels to realize trajectory detection and user identifi-cation functions. Herein, a self-powered flexible input panel with 1D output for multifunctional input detection, including letter recognition, user identification, and digit pattern detection, is proposed. The input panel is ideal for wearable human-machine interface owing to the good conformability of PU membrane to human skin and the robust performance under bending state. A 1D convolutional neural network is designed and optimized to achieve a classification accuracy of 97% on 7 letters and identification accuracy of 96.3% on five participants based on the triboelectric output from the spiral carbon grease electrodes pair of the proposed device. Dem-onstrations of harvesting energy from fabric contact and real-time digit pattern recognition are proposed to show the potential applications of the proposed input panel. These results generate fresh insight into wearable smart input panel design.