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/HfO_x/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.

中文翻译：

---

集成用于步态识别的感官记忆过程显示系统


步态是最可靠、最准确和安全的生物特征识别方法之一。然而，低功耗和低计算能力是基于可穿戴传感器的步态识别系统的两大障碍。在这项工作中，报道了一个结合了摩擦纳米发电机 （TENG）、忆阻器 （Ag/HfO_x/Pt） 和基于钙钛矿的多色 LED （PMCLED） 的集成系统，用于通过无信号和多波长设备预处理来可视化和识别脚模式。柔性 TENG 充当感觉受体，根据压力的持续时间和强度产生电压，进而促进忆阻器中的电压触发突触可塑性。PMCLED 具有阈值切换和多波长发射特性，能够对来自忆阻器的突触信号进行非线性过滤和放大，从而简化系统设计并降低背景噪声。此外，基于 5 × 5 集成设备和软件构建的神经网络验证了设备上预处理的有效性，以实现足型可视化和识别。所提出的系统能够高精度地识别设备上的预处理图像，表明在医疗保健监测和人机交互方面具有巨大潜力。