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.

Automated summary

This article introduces a self-powered flexible input panel for multifunctional input detection, including letter recognition, user identification, and digit pattern detection. The input panel has good conformability and stability, making it suitable for wearable human-machine interfaces. By optimizing the design of a convolutional neural network, high classification and identification accuracy are achieved, and potential applications for energy harvesting and real-time digit pattern recognition are proposed.