Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition.

ABSTRACT: Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals.

SUBMITTER: Guo H 

PROVIDER: S-EPMC8035911 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature