Finger–coding intelligent human–machine interaction system based on all–fabric ionic capacitive pressure sensors

Abstract

The comprehensive application of human–machine interaction (HMI) has promoted humanization in digital life. Wearable electronic devices and artificial intelligence are driving the development and transformation of HMI technology. Nevertheless, the fabrication of superior devices assisted by artificial intelligence for HMI remains challenging. We introduce an all–fabric ionic capacitive pressure sensor (AFICPS) using laser–induced graphene/fabric as the electrode and ion/fabric as the dielectric layer. The designed sensor exhibits high performance, including ultrahigh sensitivity (17.8 kPa−1@0–60 kPa, 73.3 kPa−1@60–100 kPa, and 33.8 kPa−1@100–150 kPa), wide detection range (0–150 kPa), ultrafast response and recovery times (11 and 8 ms, respectively), low detection limit (∼ 5 Pa), good mechanical stability (only 4.4 % performance loss after 5000 cycles), and excellent breathability and biocompatibility (mouse fibroblast cell survival exceeding 99 % after 48 h). All these factors contribute to the accurate detection of human physiological signals, particularly finger movements. Hence, we demonstrate a finger–coding intelligent HMI system that converts multiple gestures into 26 English letters and 10 Arabic numerals with a recognition accuracy above 98 % by using machine learning. This type of system may enhance information privacy and be of great significance in HMI and encrypted communication.