Integration of Sensory Memory Process Display System for Gait Recognition

Abstract

Gait is among the most dependable, accurate, and secure methods of biometric identification. However, high power consumption and low computing capability are two major obstacles on wearable sensors-based gait recognition system. In this work, an integrated system is reported combining a triboelectric nanogenerator (TENG), a memristor (Ag/HfOx/Pt), and perovskite-based multicolor LEDs (PMCLED) for the visualization and recognition of foot patterns through signal-on-none and multi-wavelength on-device preprocessing. The flexible TENG acts as a sensory receptor, generating voltage based on the duration and intensity of pressure, which in turn promotes voltage-triggered synaptic plasticity in the memristor. The PMCLED, with its threshold switching and multi-wavelength emission characteristics, enables nonlinear filtering and amplification of the synaptic signal from the memristor, resulting in a simplified system design and reduced background noise. Additionally, the effectiveness of on-device preprocessing is validated based on a 5 × 5 array of integrated devices and software-built neural network for foot pattern visualization and recognition. The proposed system is able to recognize the on-device preprocessed images with high accuracy, indicating great potentials in both healthcare monitoring and human-machine interaction.