标题： 实时演示：用于手势识别的类脑雷达 显示英文标题

标题： Live Demonstration: Neuromorphic Radar for Gesture Recognition

摘要： 我们提出了一种类脑雷达框架，用于使用事件驱动架构进行实时、低功耗的手势识别（HGR），该架构受到生物感知的启发。 我们的系统包括一个24 GHz多普勒雷达前端和一个定制的类脑采样器，该采样器通过异步sigma-delta编码将中频（IF）信号转换为稀疏的基于脉冲的表示。 这些事件直接由部署在Cortex-M0微控制器上的轻量级神经网络处理，从而实现实时推理，而无需进行频谱图重建。 与传统雷达HGR流水线不同，我们的架构仅在检测到有意义的运动时才激活，显著降低了内存、功耗和计算开销。 在从七名用户收集的五个手势的数据集上进行评估，我们的系统实现了>85%的实时准确率。 据我们所知，这是首次采用生物启发的异步sigma-delta编码和事件驱动处理框架用于基于雷达的HGR的工作。 显示英文摘要

摘要： We present a neuromorphic radar framework for real-time, low-power hand gesture recognition (HGR) using an event-driven architecture inspired by biological sensing. Our system comprises a 24 GHz Doppler radar front-end and a custom neuromorphic sampler that converts intermediate-frequency (IF) signals into sparse spike-based representations via asynchronous sigma-delta encoding. These events are directly processed by a lightweight neural network deployed on a Cortex-M0 microcontroller, enabling low-latency inference without requiring spectrogram reconstruction. Unlike conventional radar HGR pipelines that continuously sample and process data, our architecture activates only when meaningful motion is detected, significantly reducing memory, power, and computation overhead. Evaluated on a dataset of five gestures collected from seven users, our system achieves > 85% real-time accuracy. To the best of our knowledge, this is the first work that employs bio-inspired asynchronous sigma-delta encoding and an event-driven processing framework for radar-based HGR.